Azure Native v2.89.1, Mar 2 25

This is the latest version of Azure Native. Use the Azure Native v1 docs if using the v1 version of this package.

Azure Native v2.89.1 published on Sunday, Mar 2, 2025 by Pulumi

pulumi/pulumi-azure-native

azure-native.machinelearningservices.getJob

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This is the latest version of Azure Native. Use the Azure Native v1 docs if using the v1 version of this package.

Azure Native v2.89.1 published on Sunday, Mar 2, 2025 by Pulumi

pulumi/pulumi-azure-native

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>

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]

func LookupJob(ctx *Context, args *LookupJobArgs, opts ...InvokeOption) (*LookupJobResult, error)
func LookupJobOutput(ctx *Context, args *LookupJobOutputArgs, opts ...InvokeOption) LookupJobResultOutput

> 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)
}

public static CompletableFuture<GetJobResult> getJob(GetJobArgs args, InvokeOptions options)
public static Output<GetJobResult> getJob(GetJobArgs args, InvokeOptions options)

fn::invoke:
  function: azure-native:machinelearningservices:getJob
  arguments:
    # arguments dictionary

The following arguments are supported:

Id string: The name and identifier for the Job. This is case-sensitive.
ResourceGroupName string: The name of the resource group. The name is case insensitive.
WorkspaceName string: Name of Azure Machine Learning workspace.

Id string: The name and identifier for the Job. This is case-sensitive.
ResourceGroupName string: The name of the resource group. The name is case insensitive.
WorkspaceName string: Name of Azure Machine Learning workspace.

id String: The name and identifier for the Job. This is case-sensitive.
resourceGroupName String: The name of the resource group. The name is case insensitive.
workspaceName String: Name of Azure Machine Learning workspace.

id string: The name and identifier for the Job. This is case-sensitive.
resourceGroupName string: The name of the resource group. The name is case insensitive.
workspaceName string: Name of Azure Machine Learning workspace.

id str: The name and identifier for the Job. This is case-sensitive.
resource_group_name str: The name of the resource group. The name is case insensitive.
workspace_name str: Name of Azure Machine Learning workspace.

id String: The name and identifier for the Job. This is case-sensitive.
resourceGroupName String: The name of the resource group. The name is case insensitive.
workspaceName String: Name of Azure Machine Learning workspace.

getJob Result

The following output properties are available:

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.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"

Id string: Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}
JobBaseProperties AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | 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"

id String: Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}
jobBaseProperties AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | 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"

id string: Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}
jobBaseProperties AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | 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"

id str: Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}
job_base_properties AutoMLJobResponse | CommandJobResponse | PipelineJobResponse | 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"

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: [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 string: Status of the job.
TaskDetails Pulumi.AzureNative.MachineLearningServices.Inputs.ClassificationResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ForecastingResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ImageClassificationResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ImageClassificationMultilabelResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ImageInstanceSegmentationResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ImageObjectDetectionResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.RegressionResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.TextClassificationResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.TextClassificationMultilabelResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.TextNerResponse: [Required] This represents scenario which can be one of Tables/NLP/Image
ComponentId string: ARM resource ID of the component resource.
ComputeId string: ARM resource ID of the compute resource.
Description string: The asset description text.
DisplayName string: Display name of job.
EnvironmentId string: The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
EnvironmentVariables Dictionary<string, string>: Environment variables included in the job.
ExperimentName string: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
Identity Pulumi.AzureNative.MachineLearningServices.Inputs.AmlTokenResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ManagedIdentityResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
IsArchived bool: Is the asset archived?
Outputs Dictionary<string, object>: Mapping of output data bindings used in the job.
Properties Dictionary<string, string>: The asset property dictionary.
Resources Pulumi.AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationResponse: Compute Resource configuration for the job.
Services Dictionary<string, Pulumi.AzureNative.MachineLearningServices.Inputs.JobServiceResponse>: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
Tags Dictionary<string, string>: Tag dictionary. Tags can be added, removed, and updated.

Status string: Status of the job.
TaskDetails ClassificationResponse | ForecastingResponse | ImageClassificationResponse | ImageClassificationMultilabelResponse | ImageInstanceSegmentationResponse | ImageObjectDetectionResponse | RegressionResponse | TextClassificationResponse | TextClassificationMultilabelResponse | TextNerResponse: [Required] This represents scenario which can be one of Tables/NLP/Image
ComponentId string: ARM resource ID of the component resource.
ComputeId string: ARM resource ID of the compute resource.
Description string: The asset description text.
DisplayName string: Display name of job.
EnvironmentId string: The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
EnvironmentVariables map[string]string: Environment variables included in the job.
ExperimentName string: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
Identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
IsArchived bool: Is the asset archived?
Outputs map[string]interface{}: Mapping of output data bindings used in the job.
Properties map[string]string: The asset property dictionary.
Resources JobResourceConfigurationResponse: Compute Resource configuration for the job.
Services map[string]JobServiceResponse: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
Tags map[string]string: Tag dictionary. Tags can be added, removed, and updated.

status String: Status of the job.
taskDetails ClassificationResponse | ForecastingResponse | ImageClassificationResponse | ImageClassificationMultilabelResponse | ImageInstanceSegmentationResponse | ImageObjectDetectionResponse | RegressionResponse | TextClassificationResponse | TextClassificationMultilabelResponse | TextNerResponse: [Required] This represents scenario which can be one of Tables/NLP/Image
componentId String: ARM resource ID of the component resource.
computeId String: ARM resource ID of the compute resource.
description String: The asset description text.
displayName String: Display name of job.
environmentId String: The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
environmentVariables Map<String,String>: Environment variables included in the job.
experimentName String: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
isArchived Boolean: Is the asset archived?
outputs Map<String,Object>: Mapping of output data bindings used in the job.
properties Map<String,String>: The asset property dictionary.
resources JobResourceConfigurationResponse: Compute Resource configuration for the job.
services Map<String,JobServiceResponse>: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags Map<String,String>: Tag dictionary. Tags can be added, removed, and updated.

status string: Status of the job.
taskDetails ClassificationResponse | ForecastingResponse | ImageClassificationResponse | ImageClassificationMultilabelResponse | ImageInstanceSegmentationResponse | ImageObjectDetectionResponse | RegressionResponse | TextClassificationResponse | TextClassificationMultilabelResponse | TextNerResponse: [Required] This represents scenario which can be one of Tables/NLP/Image
componentId string: ARM resource ID of the component resource.
computeId string: ARM resource ID of the compute resource.
description string: The asset description text.
displayName string: Display name of job.
environmentId string: The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
environmentVariables {[key: string]: string}: Environment variables included in the job.
experimentName string: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
isArchived boolean: Is the asset archived?
outputs {[key: string]: CustomModelJobOutputResponse | MLFlowModelJobOutputResponse | MLTableJobOutputResponse | TritonModelJobOutputResponse | UriFileJobOutputResponse | UriFolderJobOutputResponse}: Mapping of output data bindings used in the job.
properties {[key: string]: string}: The asset property dictionary.
resources JobResourceConfigurationResponse: Compute Resource configuration for the job.
services {[key: string]: JobServiceResponse}: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags {[key: string]: string}: Tag dictionary. Tags can be added, removed, and updated.

status str: Status of the job.
task_details ClassificationResponse | ForecastingResponse | ImageClassificationResponse | ImageClassificationMultilabelResponse | ImageInstanceSegmentationResponse | ImageObjectDetectionResponse | RegressionResponse | TextClassificationResponse | TextClassificationMultilabelResponse | TextNerResponse: [Required] This represents scenario which can be one of Tables/NLP/Image
component_id str: ARM resource ID of the component resource.
compute_id str: ARM resource ID of the compute resource.
description str: The asset description text.
display_name str: Display name of job.
environment_id str: The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
environment_variables Mapping[str, str]: Environment variables included in the job.
experiment_name str: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
is_archived bool: Is the asset archived?
outputs Mapping[str, Union[CustomModelJobOutputResponse, MLFlowModelJobOutputResponse, MLTableJobOutputResponse, TritonModelJobOutputResponse, UriFileJobOutputResponse, UriFolderJobOutputResponse]]: Mapping of output data bindings used in the job.
properties Mapping[str, str]: The asset property dictionary.
resources JobResourceConfigurationResponse: Compute Resource configuration for the job.
services Mapping[str, JobServiceResponse]: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags Mapping[str, str]: Tag dictionary. Tags can be added, removed, and updated.

status String: Status of the job.
taskDetails Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map: [Required] This represents scenario which can be one of Tables/NLP/Image
componentId String: ARM resource ID of the component resource.
computeId String: ARM resource ID of the compute resource.
description String: The asset description text.
displayName String: Display name of job.
environmentId String: The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.
environmentVariables Map<String>: Environment variables included in the job.
experimentName String: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity Property Map | Property Map | Property Map: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
isArchived Boolean: Is the asset archived?
outputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>: Mapping of output data bindings used in the job.
properties Map<String>: The asset property dictionary.
resources Property Map: Compute Resource configuration for the job.
services Map<Property Map>: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags Map<String>: Tag dictionary. Tags can be added, removed, and updated.

AutoNCrossValidationsResponse

AutoSeasonalityResponse

AutoTargetLagsResponse

AutoTargetRollingWindowSizeResponse

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 Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: [Required] Training data input.
CvSplitColumnNames List<string>: Columns to use for CVSplit data.
FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
LogVerbosity string: Log verbosity for the job.
NCrossValidations Pulumi.AzureNative.MachineLearningServices.Inputs.AutoNCrossValidationsResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
PositiveLabel string: Positive label for binary metrics calculation.
PrimaryMetric string: Primary metric for the task.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
TestData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: Test data input.
TestDataSize double: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
TrainingSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ClassificationTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: Validation data inputs.
ValidationDataSize double: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
WeightColumnName string: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

TrainingData MLTableJobInputResponse: [Required] Training data input.
CvSplitColumnNames []string: Columns to use for CVSplit data.
FeaturizationSettings TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
LimitSettings TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
LogVerbosity string: Log verbosity for the job.
NCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
PositiveLabel string: Positive label for binary metrics calculation.
PrimaryMetric string: Primary metric for the task.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
TestData MLTableJobInputResponse: Test data input.
TestDataSize float64: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
TrainingSettings ClassificationTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
ValidationData MLTableJobInputResponse: Validation data inputs.
ValidationDataSize float64: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
WeightColumnName string: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

trainingData MLTableJobInputResponse: [Required] Training data input.
cvSplitColumnNames List<String>: Columns to use for CVSplit data.
featurizationSettings TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limitSettings TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
logVerbosity String: Log verbosity for the job.
nCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
positiveLabel String: Positive label for binary metrics calculation.
primaryMetric String: Primary metric for the task.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
testData MLTableJobInputResponse: Test data input.
testDataSize Double: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
trainingSettings ClassificationTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
validationData MLTableJobInputResponse: Validation data inputs.
validationDataSize Double: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
weightColumnName String: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

trainingData MLTableJobInputResponse: [Required] Training data input.
cvSplitColumnNames string[]: Columns to use for CVSplit data.
featurizationSettings TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limitSettings TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
logVerbosity string: Log verbosity for the job.
nCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
positiveLabel string: Positive label for binary metrics calculation.
primaryMetric string: Primary metric for the task.
targetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
testData MLTableJobInputResponse: Test data input.
testDataSize number: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
trainingSettings ClassificationTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
validationData MLTableJobInputResponse: Validation data inputs.
validationDataSize number: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
weightColumnName string: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

training_data MLTableJobInputResponse: [Required] Training data input.
cv_split_column_names Sequence[str]: Columns to use for CVSplit data.
featurization_settings TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limit_settings TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
log_verbosity str: Log verbosity for the job.
n_cross_validations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
positive_label str: Positive label for binary metrics calculation.
primary_metric str: Primary metric for the task.
target_column_name str: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
test_data MLTableJobInputResponse: Test data input.
test_data_size float: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
training_settings ClassificationTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
validation_data MLTableJobInputResponse: Validation data inputs.
validation_data_size float: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
weight_column_name str: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

trainingData Property Map: [Required] Training data input.
cvSplitColumnNames List<String>: Columns to use for CVSplit data.
featurizationSettings Property Map: Featurization inputs needed for AutoML job.
limitSettings Property Map: Execution constraints for AutoMLJob.
logVerbosity String: Log verbosity for the job.
nCrossValidations Property Map | Property Map: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
positiveLabel String: Positive label for binary metrics calculation.
primaryMetric String: Primary metric for the task.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
testData Property Map: Test data input.
testDataSize Number: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
trainingSettings Property Map: Inputs for training phase for an AutoML Job.
validationData Property Map: Validation data inputs.
validationDataSize Number: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
weightColumnName String: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

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 string: [Required] The command to execute on startup of the job. eg. "python train.py"
EnvironmentId string: [Required] The ARM resource ID of the Environment specification for the job.
Parameters object: Input parameters.
Status string: Status of the job.
CodeId string: ARM resource ID of the code asset.
ComponentId string: ARM resource ID of the component resource.
ComputeId string: ARM resource ID of the compute resource.
Description string: The asset description text.
DisplayName string: Display name of job.
Distribution Pulumi.AzureNative.MachineLearningServices.Inputs.MpiResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.PyTorchResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.TensorFlowResponse: Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
EnvironmentVariables Dictionary<string, string>: Environment variables included in the job.
ExperimentName string: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
Identity Pulumi.AzureNative.MachineLearningServices.Inputs.AmlTokenResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ManagedIdentityResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
Inputs Dictionary<string, object>: Mapping of input data bindings used in the job.
IsArchived bool: Is the asset archived?
Limits Pulumi.AzureNative.MachineLearningServices.Inputs.CommandJobLimitsResponse: Command Job limit.
Outputs Dictionary<string, object>: Mapping of output data bindings used in the job.
Properties Dictionary<string, string>: The asset property dictionary.
Resources Pulumi.AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationResponse: Compute Resource configuration for the job.
Services Dictionary<string, Pulumi.AzureNative.MachineLearningServices.Inputs.JobServiceResponse>: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
Tags Dictionary<string, string>: Tag dictionary. Tags can be added, removed, and updated.

Command string: [Required] The command to execute on startup of the job. eg. "python train.py"
EnvironmentId string: [Required] The ARM resource ID of the Environment specification for the job.
Parameters interface{}: Input parameters.
Status string: Status of the job.
CodeId string: ARM resource ID of the code asset.
ComponentId string: ARM resource ID of the component resource.
ComputeId string: ARM resource ID of the compute resource.
Description string: The asset description text.
DisplayName string: Display name of job.
Distribution MpiResponse | PyTorchResponse | TensorFlowResponse: Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
EnvironmentVariables map[string]string: Environment variables included in the job.
ExperimentName string: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
Identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
Inputs map[string]interface{}: Mapping of input data bindings used in the job.
IsArchived bool: Is the asset archived?
Limits CommandJobLimitsResponse: Command Job limit.
Outputs map[string]interface{}: Mapping of output data bindings used in the job.
Properties map[string]string: The asset property dictionary.
Resources JobResourceConfigurationResponse: Compute Resource configuration for the job.
Services map[string]JobServiceResponse: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
Tags map[string]string: Tag dictionary. Tags can be added, removed, and updated.

command String: [Required] The command to execute on startup of the job. eg. "python train.py"
environmentId String: [Required] The ARM resource ID of the Environment specification for the job.
parameters Object: Input parameters.
status String: Status of the job.
codeId String: ARM resource ID of the code asset.
componentId String: ARM resource ID of the component resource.
computeId String: ARM resource ID of the compute resource.
description String: The asset description text.
displayName String: Display name of job.
distribution MpiResponse | PyTorchResponse | TensorFlowResponse: Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
environmentVariables Map<String,String>: Environment variables included in the job.
experimentName String: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs Map<String,Object>: Mapping of input data bindings used in the job.
isArchived Boolean: Is the asset archived?
limits CommandJobLimitsResponse: Command Job limit.
outputs Map<String,Object>: Mapping of output data bindings used in the job.
properties Map<String,String>: The asset property dictionary.
resources JobResourceConfigurationResponse: Compute Resource configuration for the job.
services Map<String,JobServiceResponse>: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags Map<String,String>: Tag dictionary. Tags can be added, removed, and updated.

command string: [Required] The command to execute on startup of the job. eg. "python train.py"
environmentId string: [Required] The ARM resource ID of the Environment specification for the job.
parameters any: Input parameters.
status string: Status of the job.
codeId string: ARM resource ID of the code asset.
componentId string: ARM resource ID of the component resource.
computeId string: ARM resource ID of the compute resource.
description string: The asset description text.
displayName string: Display name of job.
distribution MpiResponse | PyTorchResponse | TensorFlowResponse: Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
environmentVariables {[key: string]: string}: Environment variables included in the job.
experimentName string: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs {[key: string]: CustomModelJobInputResponse | LiteralJobInputResponse | MLFlowModelJobInputResponse | MLTableJobInputResponse | TritonModelJobInputResponse | UriFileJobInputResponse | UriFolderJobInputResponse}: Mapping of input data bindings used in the job.
isArchived boolean: Is the asset archived?
limits CommandJobLimitsResponse: Command Job limit.
outputs {[key: string]: CustomModelJobOutputResponse | MLFlowModelJobOutputResponse | MLTableJobOutputResponse | TritonModelJobOutputResponse | UriFileJobOutputResponse | UriFolderJobOutputResponse}: Mapping of output data bindings used in the job.
properties {[key: string]: string}: The asset property dictionary.
resources JobResourceConfigurationResponse: Compute Resource configuration for the job.
services {[key: string]: JobServiceResponse}: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags {[key: string]: string}: Tag dictionary. Tags can be added, removed, and updated.

command str: [Required] The command to execute on startup of the job. eg. "python train.py"
environment_id str: [Required] The ARM resource ID of the Environment specification for the job.
parameters Any: Input parameters.
status str: Status of the job.
code_id str: ARM resource ID of the code asset.
component_id str: ARM resource ID of the component resource.
compute_id str: ARM resource ID of the compute resource.
description str: The asset description text.
display_name str: Display name of job.
distribution MpiResponse | PyTorchResponse | TensorFlowResponse: Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
environment_variables Mapping[str, str]: Environment variables included in the job.
experiment_name str: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs Mapping[str, Union[CustomModelJobInputResponse, LiteralJobInputResponse, MLFlowModelJobInputResponse, MLTableJobInputResponse, TritonModelJobInputResponse, UriFileJobInputResponse, UriFolderJobInputResponse]]: Mapping of input data bindings used in the job.
is_archived bool: Is the asset archived?
limits CommandJobLimitsResponse: Command Job limit.
outputs Mapping[str, Union[CustomModelJobOutputResponse, MLFlowModelJobOutputResponse, MLTableJobOutputResponse, TritonModelJobOutputResponse, UriFileJobOutputResponse, UriFolderJobOutputResponse]]: Mapping of output data bindings used in the job.
properties Mapping[str, str]: The asset property dictionary.
resources JobResourceConfigurationResponse: Compute Resource configuration for the job.
services Mapping[str, JobServiceResponse]: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags Mapping[str, str]: Tag dictionary. Tags can be added, removed, and updated.

command String: [Required] The command to execute on startup of the job. eg. "python train.py"
environmentId String: [Required] The ARM resource ID of the Environment specification for the job.
parameters Any: Input parameters.
status String: Status of the job.
codeId String: ARM resource ID of the code asset.
componentId String: ARM resource ID of the component resource.
computeId String: ARM resource ID of the compute resource.
description String: The asset description text.
displayName String: Display name of job.
distribution Property Map | Property Map | Property Map: Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
environmentVariables Map<String>: Environment variables included in the job.
experimentName String: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity Property Map | Property Map | Property Map: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>: Mapping of input data bindings used in the job.
isArchived Boolean: Is the asset archived?
limits Property Map: Command Job limit.
outputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>: Mapping of output data bindings used in the job.
properties Map<String>: The asset property dictionary.
resources Property Map: Compute Resource configuration for the job.
services Map<Property Map>: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags Map<String>: Tag dictionary. Tags can be added, removed, and updated.

CustomForecastHorizonResponse

Value int: [Required] Forecast horizon value.

Value int: [Required] Forecast horizon value.

value Integer: [Required] Forecast horizon value.

value number: [Required] Forecast horizon value.

value int: [Required] Forecast horizon value.

value Number: [Required] Forecast horizon value.

CustomModelJobInputResponse

Uri string: [Required] Input Asset URI.
Description string: Description for the input.
Mode string: Input Asset Delivery Mode.

Uri string: [Required] Input Asset URI.
Description string: Description for the input.
Mode string: Input Asset Delivery Mode.

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

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

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

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

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 int: [Required] N-Cross validations value.

Value int: [Required] N-Cross validations value.

value Integer: [Required] N-Cross validations value.

value number: [Required] N-Cross validations value.

value int: [Required] N-Cross validations value.

value Number: [Required] N-Cross validations value.

CustomSeasonalityResponse

Value int: [Required] Seasonality value.

Value int: [Required] Seasonality value.

value Integer: [Required] Seasonality value.

value number: [Required] Seasonality value.

value int: [Required] Seasonality value.

value Number: [Required] Seasonality value.

CustomTargetLagsResponse

Values List<int>: [Required] Set target lags values.

Values []int: [Required] Set target lags values.

values List<Integer>: [Required] Set target lags values.

values number[]: [Required] Set target lags values.

values Sequence[int]: [Required] Set target lags values.

values List<Number>: [Required] Set target lags values.

CustomTargetRollingWindowSizeResponse

Value int: [Required] TargetRollingWindowSize value.

Value int: [Required] TargetRollingWindowSize value.

value Integer: [Required] TargetRollingWindowSize value.

value number: [Required] TargetRollingWindowSize value.

value int: [Required] TargetRollingWindowSize value.

value Number: [Required] TargetRollingWindowSize value.

ForecastingResponse

TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: [Required] Training data input.
CvSplitColumnNames List<string>: Columns to use for CVSplit data.
FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
ForecastingSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ForecastingSettingsResponse: Forecasting task specific inputs.
LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
LogVerbosity string: Log verbosity for the job.
NCrossValidations Pulumi.AzureNative.MachineLearningServices.Inputs.AutoNCrossValidationsResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
PrimaryMetric string: Primary metric for forecasting task.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
TestData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: Test data input.
TestDataSize double: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
TrainingSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ForecastingTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: Validation data inputs.
ValidationDataSize double: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
WeightColumnName string: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

TrainingData MLTableJobInputResponse: [Required] Training data input.
CvSplitColumnNames []string: Columns to use for CVSplit data.
FeaturizationSettings TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
ForecastingSettings ForecastingSettingsResponse: Forecasting task specific inputs.
LimitSettings TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
LogVerbosity string: Log verbosity for the job.
NCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
PrimaryMetric string: Primary metric for forecasting task.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
TestData MLTableJobInputResponse: Test data input.
TestDataSize float64: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
TrainingSettings ForecastingTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
ValidationData MLTableJobInputResponse: Validation data inputs.
ValidationDataSize float64: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
WeightColumnName string: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

trainingData MLTableJobInputResponse: [Required] Training data input.
cvSplitColumnNames List<String>: Columns to use for CVSplit data.
featurizationSettings TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
forecastingSettings ForecastingSettingsResponse: Forecasting task specific inputs.
limitSettings TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
logVerbosity String: Log verbosity for the job.
nCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
primaryMetric String: Primary metric for forecasting task.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
testData MLTableJobInputResponse: Test data input.
testDataSize Double: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
trainingSettings ForecastingTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
validationData MLTableJobInputResponse: Validation data inputs.
validationDataSize Double: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
weightColumnName String: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

trainingData MLTableJobInputResponse: [Required] Training data input.
cvSplitColumnNames string[]: Columns to use for CVSplit data.
featurizationSettings TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
forecastingSettings ForecastingSettingsResponse: Forecasting task specific inputs.
limitSettings TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
logVerbosity string: Log verbosity for the job.
nCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
primaryMetric string: Primary metric for forecasting task.
targetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
testData MLTableJobInputResponse: Test data input.
testDataSize number: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
trainingSettings ForecastingTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
validationData MLTableJobInputResponse: Validation data inputs.
validationDataSize number: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
weightColumnName string: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

training_data MLTableJobInputResponse: [Required] Training data input.
cv_split_column_names Sequence[str]: Columns to use for CVSplit data.
featurization_settings TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
forecasting_settings ForecastingSettingsResponse: Forecasting task specific inputs.
limit_settings TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
log_verbosity str: Log verbosity for the job.
n_cross_validations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
primary_metric str: Primary metric for forecasting task.
target_column_name str: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
test_data MLTableJobInputResponse: Test data input.
test_data_size float: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
training_settings ForecastingTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
validation_data MLTableJobInputResponse: Validation data inputs.
validation_data_size float: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
weight_column_name str: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

trainingData Property Map: [Required] Training data input.
cvSplitColumnNames List<String>: Columns to use for CVSplit data.
featurizationSettings Property Map: Featurization inputs needed for AutoML job.
forecastingSettings Property Map: Forecasting task specific inputs.
limitSettings Property Map: Execution constraints for AutoMLJob.
logVerbosity String: Log verbosity for the job.
nCrossValidations Property Map | Property Map: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
primaryMetric String: Primary metric for forecasting task.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
testData Property Map: Test data input.
testDataSize Number: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
trainingSettings Property Map: Inputs for training phase for an AutoML Job.
validationData Property Map: Validation data inputs.
validationDataSize Number: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
weightColumnName String: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

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 Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: [Required] Training data input.
LogVerbosity string: Log verbosity for the job.
ModelSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelSettingsClassificationResponse: Settings used for training the model.
PrimaryMetric string: Primary metric to optimize for this task.
SearchSpace List<Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsClassificationResponse>: Search space for sampling different combinations of models and their hyperparameters.
SweepSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: Validation data inputs.
ValidationDataSize double: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

LimitSettings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
TrainingData MLTableJobInputResponse: [Required] Training data input.
LogVerbosity string: Log verbosity for the job.
ModelSettings ImageModelSettingsClassificationResponse: Settings used for training the model.
PrimaryMetric string: Primary metric to optimize for this task.
SearchSpace []ImageModelDistributionSettingsClassificationResponse: Search space for sampling different combinations of models and their hyperparameters.
SweepSettings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
ValidationData MLTableJobInputResponse: Validation data inputs.
ValidationDataSize float64: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limitSettings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
trainingData MLTableJobInputResponse: [Required] Training data input.
logVerbosity String: Log verbosity for the job.
modelSettings ImageModelSettingsClassificationResponse: Settings used for training the model.
primaryMetric String: Primary metric to optimize for this task.
searchSpace List<ImageModelDistributionSettingsClassificationResponse>: Search space for sampling different combinations of models and their hyperparameters.
sweepSettings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData MLTableJobInputResponse: Validation data inputs.
validationDataSize Double: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limitSettings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
trainingData MLTableJobInputResponse: [Required] Training data input.
logVerbosity string: Log verbosity for the job.
modelSettings ImageModelSettingsClassificationResponse: Settings used for training the model.
primaryMetric string: Primary metric to optimize for this task.
searchSpace ImageModelDistributionSettingsClassificationResponse[]: Search space for sampling different combinations of models and their hyperparameters.
sweepSettings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
targetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData MLTableJobInputResponse: Validation data inputs.
validationDataSize number: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limit_settings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
training_data MLTableJobInputResponse: [Required] Training data input.
log_verbosity str: Log verbosity for the job.
model_settings ImageModelSettingsClassificationResponse: Settings used for training the model.
primary_metric str: Primary metric to optimize for this task.
search_space Sequence[ImageModelDistributionSettingsClassificationResponse]: Search space for sampling different combinations of models and their hyperparameters.
sweep_settings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
target_column_name str: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validation_data MLTableJobInputResponse: Validation data inputs.
validation_data_size float: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limitSettings Property Map: [Required] Limit settings for the AutoML job.
trainingData Property Map: [Required] Training data input.
logVerbosity String: Log verbosity for the job.
modelSettings Property Map: Settings used for training the model.
primaryMetric String: Primary metric to optimize for this task.
searchSpace List<Property Map>: Search space for sampling different combinations of models and their hyperparameters.
sweepSettings Property Map: Model sweeping and hyperparameter sweeping related settings.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData Property Map: Validation data inputs.
validationDataSize Number: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

ImageClassificationResponse

LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: [Required] Training data input.
LogVerbosity string: Log verbosity for the job.
ModelSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelSettingsClassificationResponse: Settings used for training the model.
PrimaryMetric string: Primary metric to optimize for this task.
SearchSpace List<Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsClassificationResponse>: Search space for sampling different combinations of models and their hyperparameters.
SweepSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: Validation data inputs.
ValidationDataSize double: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

LimitSettings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
TrainingData MLTableJobInputResponse: [Required] Training data input.
LogVerbosity string: Log verbosity for the job.
ModelSettings ImageModelSettingsClassificationResponse: Settings used for training the model.
PrimaryMetric string: Primary metric to optimize for this task.
SearchSpace []ImageModelDistributionSettingsClassificationResponse: Search space for sampling different combinations of models and their hyperparameters.
SweepSettings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
ValidationData MLTableJobInputResponse: Validation data inputs.
ValidationDataSize float64: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limitSettings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
trainingData MLTableJobInputResponse: [Required] Training data input.
logVerbosity String: Log verbosity for the job.
modelSettings ImageModelSettingsClassificationResponse: Settings used for training the model.
primaryMetric String: Primary metric to optimize for this task.
searchSpace List<ImageModelDistributionSettingsClassificationResponse>: Search space for sampling different combinations of models and their hyperparameters.
sweepSettings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData MLTableJobInputResponse: Validation data inputs.
validationDataSize Double: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limitSettings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
trainingData MLTableJobInputResponse: [Required] Training data input.
logVerbosity string: Log verbosity for the job.
modelSettings ImageModelSettingsClassificationResponse: Settings used for training the model.
primaryMetric string: Primary metric to optimize for this task.
searchSpace ImageModelDistributionSettingsClassificationResponse[]: Search space for sampling different combinations of models and their hyperparameters.
sweepSettings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
targetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData MLTableJobInputResponse: Validation data inputs.
validationDataSize number: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limit_settings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
training_data MLTableJobInputResponse: [Required] Training data input.
log_verbosity str: Log verbosity for the job.
model_settings ImageModelSettingsClassificationResponse: Settings used for training the model.
primary_metric str: Primary metric to optimize for this task.
search_space Sequence[ImageModelDistributionSettingsClassificationResponse]: Search space for sampling different combinations of models and their hyperparameters.
sweep_settings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
target_column_name str: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validation_data MLTableJobInputResponse: Validation data inputs.
validation_data_size float: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limitSettings Property Map: [Required] Limit settings for the AutoML job.
trainingData Property Map: [Required] Training data input.
logVerbosity String: Log verbosity for the job.
modelSettings Property Map: Settings used for training the model.
primaryMetric String: Primary metric to optimize for this task.
searchSpace List<Property Map>: Search space for sampling different combinations of models and their hyperparameters.
sweepSettings Property Map: Model sweeping and hyperparameter sweeping related settings.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData Property Map: Validation data inputs.
validationDataSize Number: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

ImageInstanceSegmentationResponse

LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: [Required] Training data input.
LogVerbosity string: Log verbosity for the job.
ModelSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelSettingsObjectDetectionResponse: Settings used for training the model.
PrimaryMetric string: Primary metric to optimize for this task.
SearchSpace List<Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsObjectDetectionResponse>: Search space for sampling different combinations of models and their hyperparameters.
SweepSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: Validation data inputs.
ValidationDataSize double: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

LimitSettings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
TrainingData MLTableJobInputResponse: [Required] Training data input.
LogVerbosity string: Log verbosity for the job.
ModelSettings ImageModelSettingsObjectDetectionResponse: Settings used for training the model.
PrimaryMetric string: Primary metric to optimize for this task.
SearchSpace []ImageModelDistributionSettingsObjectDetectionResponse: Search space for sampling different combinations of models and their hyperparameters.
SweepSettings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
ValidationData MLTableJobInputResponse: Validation data inputs.
ValidationDataSize float64: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limitSettings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
trainingData MLTableJobInputResponse: [Required] Training data input.
logVerbosity String: Log verbosity for the job.
modelSettings ImageModelSettingsObjectDetectionResponse: Settings used for training the model.
primaryMetric String: Primary metric to optimize for this task.
searchSpace List<ImageModelDistributionSettingsObjectDetectionResponse>: Search space for sampling different combinations of models and their hyperparameters.
sweepSettings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData MLTableJobInputResponse: Validation data inputs.
validationDataSize Double: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limitSettings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
trainingData MLTableJobInputResponse: [Required] Training data input.
logVerbosity string: Log verbosity for the job.
modelSettings ImageModelSettingsObjectDetectionResponse: Settings used for training the model.
primaryMetric string: Primary metric to optimize for this task.
searchSpace ImageModelDistributionSettingsObjectDetectionResponse[]: Search space for sampling different combinations of models and their hyperparameters.
sweepSettings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
targetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData MLTableJobInputResponse: Validation data inputs.
validationDataSize number: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limit_settings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
training_data MLTableJobInputResponse: [Required] Training data input.
log_verbosity str: Log verbosity for the job.
model_settings ImageModelSettingsObjectDetectionResponse: Settings used for training the model.
primary_metric str: Primary metric to optimize for this task.
search_space Sequence[ImageModelDistributionSettingsObjectDetectionResponse]: Search space for sampling different combinations of models and their hyperparameters.
sweep_settings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
target_column_name str: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validation_data MLTableJobInputResponse: Validation data inputs.
validation_data_size float: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limitSettings Property Map: [Required] Limit settings for the AutoML job.
trainingData Property Map: [Required] Training data input.
logVerbosity String: Log verbosity for the job.
modelSettings Property Map: Settings used for training the model.
primaryMetric String: Primary metric to optimize for this task.
searchSpace List<Property Map>: Search space for sampling different combinations of models and their hyperparameters.
sweepSettings Property Map: Model sweeping and hyperparameter sweeping related settings.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData Property Map: Validation data inputs.
validationDataSize Number: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

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 Pulumi.AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: [Required] Training data input.
LogVerbosity string: Log verbosity for the job.
ModelSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelSettingsObjectDetectionResponse: Settings used for training the model.
PrimaryMetric string: Primary metric to optimize for this task.
SearchSpace List<Pulumi.AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsObjectDetectionResponse>: Search space for sampling different combinations of models and their hyperparameters.
SweepSettings Pulumi.AzureNative.MachineLearningServices.Inputs.ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: Validation data inputs.
ValidationDataSize double: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

LimitSettings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
TrainingData MLTableJobInputResponse: [Required] Training data input.
LogVerbosity string: Log verbosity for the job.
ModelSettings ImageModelSettingsObjectDetectionResponse: Settings used for training the model.
PrimaryMetric string: Primary metric to optimize for this task.
SearchSpace []ImageModelDistributionSettingsObjectDetectionResponse: Search space for sampling different combinations of models and their hyperparameters.
SweepSettings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
ValidationData MLTableJobInputResponse: Validation data inputs.
ValidationDataSize float64: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limitSettings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
trainingData MLTableJobInputResponse: [Required] Training data input.
logVerbosity String: Log verbosity for the job.
modelSettings ImageModelSettingsObjectDetectionResponse: Settings used for training the model.
primaryMetric String: Primary metric to optimize for this task.
searchSpace List<ImageModelDistributionSettingsObjectDetectionResponse>: Search space for sampling different combinations of models and their hyperparameters.
sweepSettings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData MLTableJobInputResponse: Validation data inputs.
validationDataSize Double: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limitSettings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
trainingData MLTableJobInputResponse: [Required] Training data input.
logVerbosity string: Log verbosity for the job.
modelSettings ImageModelSettingsObjectDetectionResponse: Settings used for training the model.
primaryMetric string: Primary metric to optimize for this task.
searchSpace ImageModelDistributionSettingsObjectDetectionResponse[]: Search space for sampling different combinations of models and their hyperparameters.
sweepSettings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
targetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData MLTableJobInputResponse: Validation data inputs.
validationDataSize number: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limit_settings ImageLimitSettingsResponse: [Required] Limit settings for the AutoML job.
training_data MLTableJobInputResponse: [Required] Training data input.
log_verbosity str: Log verbosity for the job.
model_settings ImageModelSettingsObjectDetectionResponse: Settings used for training the model.
primary_metric str: Primary metric to optimize for this task.
search_space Sequence[ImageModelDistributionSettingsObjectDetectionResponse]: Search space for sampling different combinations of models and their hyperparameters.
sweep_settings ImageSweepSettingsResponse: Model sweeping and hyperparameter sweeping related settings.
target_column_name str: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validation_data MLTableJobInputResponse: Validation data inputs.
validation_data_size float: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

limitSettings Property Map: [Required] Limit settings for the AutoML job.
trainingData Property Map: [Required] Training data input.
logVerbosity String: Log verbosity for the job.
modelSettings Property Map: Settings used for training the model.
primaryMetric String: Primary metric to optimize for this task.
searchSpace List<Property Map>: Search space for sampling different combinations of models and their hyperparameters.
sweepSettings Property Map: Model sweeping and hyperparameter sweeping related settings.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData Property Map: Validation data inputs.
validationDataSize Number: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.

ImageSweepSettingsResponse

SamplingAlgorithm string: [Required] Type of the hyperparameter sampling algorithms.
EarlyTermination Pulumi.AzureNative.MachineLearningServices.Inputs.BanditPolicyResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.MedianStoppingPolicyResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.TruncationSelectionPolicyResponse: Type of early termination policy.

SamplingAlgorithm string: [Required] Type of the hyperparameter sampling algorithms.
EarlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse: Type of early termination policy.

samplingAlgorithm String: [Required] Type of the hyperparameter sampling algorithms.
earlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse: Type of early termination policy.

samplingAlgorithm string: [Required] Type of the hyperparameter sampling algorithms.
earlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse: Type of early termination policy.

sampling_algorithm str: [Required] Type of the hyperparameter sampling algorithms.
early_termination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse: Type of early termination policy.

samplingAlgorithm String: [Required] Type of the hyperparameter sampling algorithms.
earlyTermination Property Map | Property Map | Property Map: Type of early termination policy.

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 string: Any error in the service.
Status string: Status of endpoint.
Endpoint string: Url for endpoint.
JobServiceType string: Endpoint type.
Nodes Pulumi.AzureNative.MachineLearningServices.Inputs.AllNodesResponse: Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
Port int: Port for endpoint.
Properties Dictionary<string, string>: Additional properties to set on the endpoint.

ErrorMessage string: Any error in the service.
Status string: Status of endpoint.
Endpoint string: Url for endpoint.
JobServiceType string: Endpoint type.
Nodes AllNodesResponse: Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
Port int: Port for endpoint.
Properties map[string]string: Additional properties to set on the endpoint.

errorMessage String: Any error in the service.
status String: Status of endpoint.
endpoint String: Url for endpoint.
jobServiceType String: Endpoint type.
nodes AllNodesResponse: Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
port Integer: Port for endpoint.
properties Map<String,String>: Additional properties to set on the endpoint.

errorMessage string: Any error in the service.
status string: Status of endpoint.
endpoint string: Url for endpoint.
jobServiceType string: Endpoint type.
nodes AllNodesResponse: Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
port number: Port for endpoint.
properties {[key: string]: string}: Additional properties to set on the endpoint.

error_message str: Any error in the service.
status str: Status of endpoint.
endpoint str: Url for endpoint.
job_service_type str: Endpoint type.
nodes AllNodesResponse: Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
port int: Port for endpoint.
properties Mapping[str, str]: Additional properties to set on the endpoint.

errorMessage String: Any error in the service.
status String: Status of endpoint.
endpoint String: Url for endpoint.
jobServiceType String: Endpoint type.
nodes Property Map: Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.
port Number: Port for endpoint.
properties Map<String>: Additional properties to set on the endpoint.

LiteralJobInputResponse

Value string: [Required] Literal value for the input.
Description string: Description for the input.

Value string: [Required] Literal value for the input.
Description string: Description for the input.

value String: [Required] Literal value for the input.
description String: Description for the input.

value string: [Required] Literal value for the input.
description string: Description for the input.

value str: [Required] Literal value for the input.
description str: Description for the input.

value String: [Required] Literal value for the input.
description String: Description for the input.

MLFlowModelJobInputResponse

Uri string: [Required] Input Asset URI.
Description string: Description for the input.
Mode string: Input Asset Delivery Mode.

Uri string: [Required] Input Asset URI.
Description string: Description for the input.
Mode string: Input Asset Delivery Mode.

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

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

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

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

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 string: [Required] Input Asset URI.
Description string: Description for the input.
Mode string: Input Asset Delivery Mode.

Uri string: [Required] Input Asset URI.
Description string: Description for the input.
Mode string: Input Asset Delivery Mode.

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

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

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

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

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.

ObjectiveResponse

Goal string: [Required] Defines supported metric goals for hyperparameter tuning
PrimaryMetric string: [Required] Name of the metric to optimize.

Goal string: [Required] Defines supported metric goals for hyperparameter tuning
PrimaryMetric string: [Required] Name of the metric to optimize.

goal String: [Required] Defines supported metric goals for hyperparameter tuning
primaryMetric String: [Required] Name of the metric to optimize.

goal string: [Required] Defines supported metric goals for hyperparameter tuning
primaryMetric string: [Required] Name of the metric to optimize.

goal str: [Required] Defines supported metric goals for hyperparameter tuning
primary_metric str: [Required] Name of the metric to optimize.

goal String: [Required] Defines supported metric goals for hyperparameter tuning
primaryMetric String: [Required] Name of the metric to optimize.

PipelineJobResponse

Status string: Status of the job.
ComponentId string: ARM resource ID of the component resource.
ComputeId string: ARM resource ID of the compute resource.
Description string: The asset description text.
DisplayName string: Display name of job.
ExperimentName string: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
Identity Pulumi.AzureNative.MachineLearningServices.Inputs.AmlTokenResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ManagedIdentityResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
Inputs Dictionary<string, object>: Inputs for the pipeline job.
IsArchived bool: Is the asset archived?
Jobs Dictionary<string, object>: Jobs construct the Pipeline Job.
Outputs Dictionary<string, object>: Outputs for the pipeline job
Properties Dictionary<string, string>: The asset property dictionary.
Services Dictionary<string, Pulumi.AzureNative.MachineLearningServices.Inputs.JobServiceResponse>: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
Settings object: Pipeline settings, for things like ContinueRunOnStepFailure etc.
SourceJobId string: ARM resource ID of source job.
Tags Dictionary<string, string>: Tag dictionary. Tags can be added, removed, and updated.

Status string: Status of the job.
ComponentId string: ARM resource ID of the component resource.
ComputeId string: ARM resource ID of the compute resource.
Description string: The asset description text.
DisplayName string: Display name of job.
ExperimentName string: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
Identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
Inputs map[string]interface{}: Inputs for the pipeline job.
IsArchived bool: Is the asset archived?
Jobs map[string]interface{}: Jobs construct the Pipeline Job.
Outputs map[string]interface{}: Outputs for the pipeline job
Properties map[string]string: The asset property dictionary.
Services map[string]JobServiceResponse: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
Settings interface{}: Pipeline settings, for things like ContinueRunOnStepFailure etc.
SourceJobId string: ARM resource ID of source job.
Tags map[string]string: Tag dictionary. Tags can be added, removed, and updated.

status String: Status of the job.
componentId String: ARM resource ID of the component resource.
computeId String: ARM resource ID of the compute resource.
description String: The asset description text.
displayName String: Display name of job.
experimentName String: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs Map<String,Object>: Inputs for the pipeline job.
isArchived Boolean: Is the asset archived?
jobs Map<String,Object>: Jobs construct the Pipeline Job.
outputs Map<String,Object>: Outputs for the pipeline job
properties Map<String,String>: The asset property dictionary.
services Map<String,JobServiceResponse>: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
settings Object: Pipeline settings, for things like ContinueRunOnStepFailure etc.
sourceJobId String: ARM resource ID of source job.
tags Map<String,String>: Tag dictionary. Tags can be added, removed, and updated.

status string: Status of the job.
componentId string: ARM resource ID of the component resource.
computeId string: ARM resource ID of the compute resource.
description string: The asset description text.
displayName string: Display name of job.
experimentName string: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs {[key: string]: CustomModelJobInputResponse | LiteralJobInputResponse | MLFlowModelJobInputResponse | MLTableJobInputResponse | TritonModelJobInputResponse | UriFileJobInputResponse | UriFolderJobInputResponse}: Inputs for the pipeline job.
isArchived boolean: Is the asset archived?
jobs {[key: string]: any}: Jobs construct the Pipeline Job.
outputs {[key: string]: CustomModelJobOutputResponse | MLFlowModelJobOutputResponse | MLTableJobOutputResponse | TritonModelJobOutputResponse | UriFileJobOutputResponse | UriFolderJobOutputResponse}: Outputs for the pipeline job
properties {[key: string]: string}: The asset property dictionary.
services {[key: string]: JobServiceResponse}: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
settings any: Pipeline settings, for things like ContinueRunOnStepFailure etc.
sourceJobId string: ARM resource ID of source job.
tags {[key: string]: string}: Tag dictionary. Tags can be added, removed, and updated.

status str: Status of the job.
component_id str: ARM resource ID of the component resource.
compute_id str: ARM resource ID of the compute resource.
description str: The asset description text.
display_name str: Display name of job.
experiment_name str: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs Mapping[str, Union[CustomModelJobInputResponse, LiteralJobInputResponse, MLFlowModelJobInputResponse, MLTableJobInputResponse, TritonModelJobInputResponse, UriFileJobInputResponse, UriFolderJobInputResponse]]: Inputs for the pipeline job.
is_archived bool: Is the asset archived?
jobs Mapping[str, Any]: Jobs construct the Pipeline Job.
outputs Mapping[str, Union[CustomModelJobOutputResponse, MLFlowModelJobOutputResponse, MLTableJobOutputResponse, TritonModelJobOutputResponse, UriFileJobOutputResponse, UriFolderJobOutputResponse]]: Outputs for the pipeline job
properties Mapping[str, str]: The asset property dictionary.
services Mapping[str, JobServiceResponse]: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
settings Any: Pipeline settings, for things like ContinueRunOnStepFailure etc.
source_job_id str: ARM resource ID of source job.
tags Mapping[str, str]: Tag dictionary. Tags can be added, removed, and updated.

status String: Status of the job.
componentId String: ARM resource ID of the component resource.
computeId String: ARM resource ID of the compute resource.
description String: The asset description text.
displayName String: Display name of job.
experimentName String: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity Property Map | Property Map | Property Map: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>: Inputs for the pipeline job.
isArchived Boolean: Is the asset archived?
jobs Map<Any>: Jobs construct the Pipeline Job.
outputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>: Outputs for the pipeline job
properties Map<String>: The asset property dictionary.
services Map<Property Map>: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
settings Any: Pipeline settings, for things like ContinueRunOnStepFailure etc.
sourceJobId String: ARM resource ID of source job.
tags Map<String>: Tag dictionary. Tags can be added, removed, and updated.

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.

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 Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: [Required] Training data input.
CvSplitColumnNames List<string>: Columns to use for CVSplit data.
FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
LogVerbosity string: Log verbosity for the job.
NCrossValidations Pulumi.AzureNative.MachineLearningServices.Inputs.AutoNCrossValidationsResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
PrimaryMetric string: Primary metric for regression task.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
TestData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: Test data input.
TestDataSize double: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
TrainingSettings Pulumi.AzureNative.MachineLearningServices.Inputs.RegressionTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: Validation data inputs.
ValidationDataSize double: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
WeightColumnName string: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

TrainingData MLTableJobInputResponse: [Required] Training data input.
CvSplitColumnNames []string: Columns to use for CVSplit data.
FeaturizationSettings TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
LimitSettings TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
LogVerbosity string: Log verbosity for the job.
NCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
PrimaryMetric string: Primary metric for regression task.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
TestData MLTableJobInputResponse: Test data input.
TestDataSize float64: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
TrainingSettings RegressionTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
ValidationData MLTableJobInputResponse: Validation data inputs.
ValidationDataSize float64: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
WeightColumnName string: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

trainingData MLTableJobInputResponse: [Required] Training data input.
cvSplitColumnNames List<String>: Columns to use for CVSplit data.
featurizationSettings TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limitSettings TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
logVerbosity String: Log verbosity for the job.
nCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
primaryMetric String: Primary metric for regression task.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
testData MLTableJobInputResponse: Test data input.
testDataSize Double: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
trainingSettings RegressionTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
validationData MLTableJobInputResponse: Validation data inputs.
validationDataSize Double: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
weightColumnName String: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

trainingData MLTableJobInputResponse: [Required] Training data input.
cvSplitColumnNames string[]: Columns to use for CVSplit data.
featurizationSettings TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limitSettings TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
logVerbosity string: Log verbosity for the job.
nCrossValidations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
primaryMetric string: Primary metric for regression task.
targetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
testData MLTableJobInputResponse: Test data input.
testDataSize number: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
trainingSettings RegressionTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
validationData MLTableJobInputResponse: Validation data inputs.
validationDataSize number: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
weightColumnName string: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

training_data MLTableJobInputResponse: [Required] Training data input.
cv_split_column_names Sequence[str]: Columns to use for CVSplit data.
featurization_settings TableVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limit_settings TableVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
log_verbosity str: Log verbosity for the job.
n_cross_validations AutoNCrossValidationsResponse | CustomNCrossValidationsResponse: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
primary_metric str: Primary metric for regression task.
target_column_name str: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
test_data MLTableJobInputResponse: Test data input.
test_data_size float: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
training_settings RegressionTrainingSettingsResponse: Inputs for training phase for an AutoML Job.
validation_data MLTableJobInputResponse: Validation data inputs.
validation_data_size float: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
weight_column_name str: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

trainingData Property Map: [Required] Training data input.
cvSplitColumnNames List<String>: Columns to use for CVSplit data.
featurizationSettings Property Map: Featurization inputs needed for AutoML job.
limitSettings Property Map: Execution constraints for AutoMLJob.
logVerbosity String: Log verbosity for the job.
nCrossValidations Property Map | Property Map: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
primaryMetric String: Primary metric for regression task.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
testData Property Map: Test data input.
testDataSize Number: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
trainingSettings Property Map: Inputs for training phase for an AutoML Job.
validationData Property Map: Validation data inputs.
validationDataSize Number: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
weightColumnName String: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.

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.

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 Pulumi.AzureNative.MachineLearningServices.Inputs.ObjectiveResponse: [Required] Optimization objective.
SamplingAlgorithm Pulumi.AzureNative.MachineLearningServices.Inputs.BayesianSamplingAlgorithmResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.GridSamplingAlgorithmResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.RandomSamplingAlgorithmResponse: [Required] The hyperparameter sampling algorithm
SearchSpace object: [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
Status string: Status of the job.
Trial Pulumi.AzureNative.MachineLearningServices.Inputs.TrialComponentResponse: [Required] Trial component definition.
ComponentId string: ARM resource ID of the component resource.
ComputeId string: ARM resource ID of the compute resource.
Description string: The asset description text.
DisplayName string: Display name of job.
EarlyTermination Pulumi.AzureNative.MachineLearningServices.Inputs.BanditPolicyResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.MedianStoppingPolicyResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.TruncationSelectionPolicyResponse: Early termination policies enable canceling poor-performing runs before they complete
ExperimentName string: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
Identity Pulumi.AzureNative.MachineLearningServices.Inputs.AmlTokenResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.ManagedIdentityResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
Inputs Dictionary<string, object>: Mapping of input data bindings used in the job.
IsArchived bool: Is the asset archived?
Limits Pulumi.AzureNative.MachineLearningServices.Inputs.SweepJobLimitsResponse: Sweep Job limit.
Outputs Dictionary<string, object>: Mapping of output data bindings used in the job.
Properties Dictionary<string, string>: The asset property dictionary.
Services Dictionary<string, Pulumi.AzureNative.MachineLearningServices.Inputs.JobServiceResponse>: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
Tags Dictionary<string, string>: Tag dictionary. Tags can be added, removed, and updated.

Objective ObjectiveResponse: [Required] Optimization objective.
SamplingAlgorithm BayesianSamplingAlgorithmResponse | GridSamplingAlgorithmResponse | RandomSamplingAlgorithmResponse: [Required] The hyperparameter sampling algorithm
SearchSpace interface{}: [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
Status string: Status of the job.
Trial TrialComponentResponse: [Required] Trial component definition.
ComponentId string: ARM resource ID of the component resource.
ComputeId string: ARM resource ID of the compute resource.
Description string: The asset description text.
DisplayName string: Display name of job.
EarlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse: Early termination policies enable canceling poor-performing runs before they complete
ExperimentName string: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
Identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
Inputs map[string]interface{}: Mapping of input data bindings used in the job.
IsArchived bool: Is the asset archived?
Limits SweepJobLimitsResponse: Sweep Job limit.
Outputs map[string]interface{}: Mapping of output data bindings used in the job.
Properties map[string]string: The asset property dictionary.
Services map[string]JobServiceResponse: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
Tags map[string]string: Tag dictionary. Tags can be added, removed, and updated.

objective ObjectiveResponse: [Required] Optimization objective.
samplingAlgorithm BayesianSamplingAlgorithmResponse | GridSamplingAlgorithmResponse | RandomSamplingAlgorithmResponse: [Required] The hyperparameter sampling algorithm
searchSpace Object: [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
status String: Status of the job.
trial TrialComponentResponse: [Required] Trial component definition.
componentId String: ARM resource ID of the component resource.
computeId String: ARM resource ID of the compute resource.
description String: The asset description text.
displayName String: Display name of job.
earlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse: Early termination policies enable canceling poor-performing runs before they complete
experimentName String: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs Map<String,Object>: Mapping of input data bindings used in the job.
isArchived Boolean: Is the asset archived?
limits SweepJobLimitsResponse: Sweep Job limit.
outputs Map<String,Object>: Mapping of output data bindings used in the job.
properties Map<String,String>: The asset property dictionary.
services Map<String,JobServiceResponse>: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags Map<String,String>: Tag dictionary. Tags can be added, removed, and updated.

objective ObjectiveResponse: [Required] Optimization objective.
samplingAlgorithm BayesianSamplingAlgorithmResponse | GridSamplingAlgorithmResponse | RandomSamplingAlgorithmResponse: [Required] The hyperparameter sampling algorithm
searchSpace any: [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
status string: Status of the job.
trial TrialComponentResponse: [Required] Trial component definition.
componentId string: ARM resource ID of the component resource.
computeId string: ARM resource ID of the compute resource.
description string: The asset description text.
displayName string: Display name of job.
earlyTermination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse: Early termination policies enable canceling poor-performing runs before they complete
experimentName string: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs {[key: string]: CustomModelJobInputResponse | LiteralJobInputResponse | MLFlowModelJobInputResponse | MLTableJobInputResponse | TritonModelJobInputResponse | UriFileJobInputResponse | UriFolderJobInputResponse}: Mapping of input data bindings used in the job.
isArchived boolean: Is the asset archived?
limits SweepJobLimitsResponse: Sweep Job limit.
outputs {[key: string]: CustomModelJobOutputResponse | MLFlowModelJobOutputResponse | MLTableJobOutputResponse | TritonModelJobOutputResponse | UriFileJobOutputResponse | UriFolderJobOutputResponse}: Mapping of output data bindings used in the job.
properties {[key: string]: string}: The asset property dictionary.
services {[key: string]: JobServiceResponse}: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags {[key: string]: string}: Tag dictionary. Tags can be added, removed, and updated.

objective ObjectiveResponse: [Required] Optimization objective.
sampling_algorithm BayesianSamplingAlgorithmResponse | GridSamplingAlgorithmResponse | RandomSamplingAlgorithmResponse: [Required] The hyperparameter sampling algorithm
search_space Any: [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
status str: Status of the job.
trial TrialComponentResponse: [Required] Trial component definition.
component_id str: ARM resource ID of the component resource.
compute_id str: ARM resource ID of the compute resource.
description str: The asset description text.
display_name str: Display name of job.
early_termination BanditPolicyResponse | MedianStoppingPolicyResponse | TruncationSelectionPolicyResponse: Early termination policies enable canceling poor-performing runs before they complete
experiment_name str: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity AmlTokenResponse | ManagedIdentityResponse | UserIdentityResponse: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs Mapping[str, Union[CustomModelJobInputResponse, LiteralJobInputResponse, MLFlowModelJobInputResponse, MLTableJobInputResponse, TritonModelJobInputResponse, UriFileJobInputResponse, UriFolderJobInputResponse]]: Mapping of input data bindings used in the job.
is_archived bool: Is the asset archived?
limits SweepJobLimitsResponse: Sweep Job limit.
outputs Mapping[str, Union[CustomModelJobOutputResponse, MLFlowModelJobOutputResponse, MLTableJobOutputResponse, TritonModelJobOutputResponse, UriFileJobOutputResponse, UriFolderJobOutputResponse]]: Mapping of output data bindings used in the job.
properties Mapping[str, str]: The asset property dictionary.
services Mapping[str, JobServiceResponse]: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags Mapping[str, str]: Tag dictionary. Tags can be added, removed, and updated.

objective Property Map: [Required] Optimization objective.
samplingAlgorithm Property Map | Property Map | Property Map: [Required] The hyperparameter sampling algorithm
searchSpace Any: [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
status String: Status of the job.
trial Property Map: [Required] Trial component definition.
componentId String: ARM resource ID of the component resource.
computeId String: ARM resource ID of the compute resource.
description String: The asset description text.
displayName String: Display name of job.
earlyTermination Property Map | Property Map | Property Map: Early termination policies enable canceling poor-performing runs before they complete
experimentName String: The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.
identity Property Map | Property Map | Property Map: Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.
inputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>: Mapping of input data bindings used in the job.
isArchived Boolean: Is the asset archived?
limits Property Map: Sweep Job limit.
outputs Map<Property Map | Property Map | Property Map | Property Map | Property Map | Property Map>: Mapping of output data bindings used in the job.
properties Map<String>: The asset property dictionary.
services Map<Property Map>: List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.
tags Map<String>: Tag dictionary. Tags can be added, removed, and updated.

SystemDataResponse

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 string: Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: [Required] Training data input.
FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
LogVerbosity string: Log verbosity for the job.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: Validation data inputs.

PrimaryMetric string: Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
TrainingData MLTableJobInputResponse: [Required] Training data input.
FeaturizationSettings NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
LimitSettings NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
LogVerbosity string: Log verbosity for the job.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
ValidationData MLTableJobInputResponse: Validation data inputs.

primaryMetric String: Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
trainingData MLTableJobInputResponse: [Required] Training data input.
featurizationSettings NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limitSettings NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
logVerbosity String: Log verbosity for the job.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData MLTableJobInputResponse: Validation data inputs.

primaryMetric string: Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
trainingData MLTableJobInputResponse: [Required] Training data input.
featurizationSettings NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limitSettings NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
logVerbosity string: Log verbosity for the job.
targetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData MLTableJobInputResponse: Validation data inputs.

primary_metric str: Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
training_data MLTableJobInputResponse: [Required] Training data input.
featurization_settings NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limit_settings NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
log_verbosity str: Log verbosity for the job.
target_column_name str: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validation_data MLTableJobInputResponse: Validation data inputs.

primaryMetric String: Primary metric for Text-Classification-Multilabel task. Currently only Accuracy is supported as primary metric, hence user need not set it explicitly.
trainingData Property Map: [Required] Training data input.
featurizationSettings Property Map: Featurization inputs needed for AutoML job.
limitSettings Property Map: Execution constraints for AutoMLJob.
logVerbosity String: Log verbosity for the job.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData Property Map: Validation data inputs.

TextClassificationResponse

TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: [Required] Training data input.
FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
LogVerbosity string: Log verbosity for the job.
PrimaryMetric string: Primary metric for Text-Classification task.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: Validation data inputs.

TrainingData MLTableJobInputResponse: [Required] Training data input.
FeaturizationSettings NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
LimitSettings NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
LogVerbosity string: Log verbosity for the job.
PrimaryMetric string: Primary metric for Text-Classification task.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
ValidationData MLTableJobInputResponse: Validation data inputs.

trainingData MLTableJobInputResponse: [Required] Training data input.
featurizationSettings NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limitSettings NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
logVerbosity String: Log verbosity for the job.
primaryMetric String: Primary metric for Text-Classification task.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData MLTableJobInputResponse: Validation data inputs.

trainingData MLTableJobInputResponse: [Required] Training data input.
featurizationSettings NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limitSettings NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
logVerbosity string: Log verbosity for the job.
primaryMetric string: Primary metric for Text-Classification task.
targetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData MLTableJobInputResponse: Validation data inputs.

training_data MLTableJobInputResponse: [Required] Training data input.
featurization_settings NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limit_settings NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
log_verbosity str: Log verbosity for the job.
primary_metric str: Primary metric for Text-Classification task.
target_column_name str: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validation_data MLTableJobInputResponse: Validation data inputs.

trainingData Property Map: [Required] Training data input.
featurizationSettings Property Map: Featurization inputs needed for AutoML job.
limitSettings Property Map: Execution constraints for AutoMLJob.
logVerbosity String: Log verbosity for the job.
primaryMetric String: Primary metric for Text-Classification task.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData Property Map: Validation data inputs.

TextNerResponse

PrimaryMetric string: Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
TrainingData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: [Required] Training data input.
FeaturizationSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
LimitSettings Pulumi.AzureNative.MachineLearningServices.Inputs.NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
LogVerbosity string: Log verbosity for the job.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
ValidationData Pulumi.AzureNative.MachineLearningServices.Inputs.MLTableJobInputResponse: Validation data inputs.

PrimaryMetric string: Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
TrainingData MLTableJobInputResponse: [Required] Training data input.
FeaturizationSettings NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
LimitSettings NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
LogVerbosity string: Log verbosity for the job.
TargetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
ValidationData MLTableJobInputResponse: Validation data inputs.

primaryMetric String: Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
trainingData MLTableJobInputResponse: [Required] Training data input.
featurizationSettings NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limitSettings NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
logVerbosity String: Log verbosity for the job.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData MLTableJobInputResponse: Validation data inputs.

primaryMetric string: Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
trainingData MLTableJobInputResponse: [Required] Training data input.
featurizationSettings NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limitSettings NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
logVerbosity string: Log verbosity for the job.
targetColumnName string: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData MLTableJobInputResponse: Validation data inputs.

primary_metric str: Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
training_data MLTableJobInputResponse: [Required] Training data input.
featurization_settings NlpVerticalFeaturizationSettingsResponse: Featurization inputs needed for AutoML job.
limit_settings NlpVerticalLimitSettingsResponse: Execution constraints for AutoMLJob.
log_verbosity str: Log verbosity for the job.
target_column_name str: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validation_data MLTableJobInputResponse: Validation data inputs.

primaryMetric String: Primary metric for Text-NER task. Only 'Accuracy' is supported for Text-NER, so user need not set this explicitly.
trainingData Property Map: [Required] Training data input.
featurizationSettings Property Map: Featurization inputs needed for AutoML job.
limitSettings Property Map: Execution constraints for AutoMLJob.
logVerbosity String: Log verbosity for the job.
targetColumnName String: Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
validationData Property Map: Validation data inputs.

TrialComponentResponse

Command string: [Required] The command to execute on startup of the job. eg. "python train.py"
EnvironmentId string: [Required] The ARM resource ID of the Environment specification for the job.
CodeId string: ARM resource ID of the code asset.
Distribution Pulumi.AzureNative.MachineLearningServices.Inputs.MpiResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.PyTorchResponse | Pulumi.AzureNative.MachineLearningServices.Inputs.TensorFlowResponse: Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
EnvironmentVariables Dictionary<string, string>: Environment variables included in the job.
Resources Pulumi.AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationResponse: Compute Resource configuration for the job.

Command string: [Required] The command to execute on startup of the job. eg. "python train.py"
EnvironmentId string: [Required] The ARM resource ID of the Environment specification for the job.
CodeId string: ARM resource ID of the code asset.
Distribution MpiResponse | PyTorchResponse | TensorFlowResponse: Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
EnvironmentVariables map[string]string: Environment variables included in the job.
Resources JobResourceConfigurationResponse: Compute Resource configuration for the job.

command String: [Required] The command to execute on startup of the job. eg. "python train.py"
environmentId String: [Required] The ARM resource ID of the Environment specification for the job.
codeId String: ARM resource ID of the code asset.
distribution MpiResponse | PyTorchResponse | TensorFlowResponse: Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
environmentVariables Map<String,String>: Environment variables included in the job.
resources JobResourceConfigurationResponse: Compute Resource configuration for the job.

command string: [Required] The command to execute on startup of the job. eg. "python train.py"
environmentId string: [Required] The ARM resource ID of the Environment specification for the job.
codeId string: ARM resource ID of the code asset.
distribution MpiResponse | PyTorchResponse | TensorFlowResponse: Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
environmentVariables {[key: string]: string}: Environment variables included in the job.
resources JobResourceConfigurationResponse: Compute Resource configuration for the job.

command str: [Required] The command to execute on startup of the job. eg. "python train.py"
environment_id str: [Required] The ARM resource ID of the Environment specification for the job.
code_id str: ARM resource ID of the code asset.
distribution MpiResponse | PyTorchResponse | TensorFlowResponse: Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
environment_variables Mapping[str, str]: Environment variables included in the job.
resources JobResourceConfigurationResponse: Compute Resource configuration for the job.

command String: [Required] The command to execute on startup of the job. eg. "python train.py"
environmentId String: [Required] The ARM resource ID of the Environment specification for the job.
codeId String: ARM resource ID of the code asset.
distribution Property Map | Property Map | Property Map: Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.
environmentVariables Map<String>: Environment variables included in the job.
resources Property Map: Compute Resource configuration for the job.

TritonModelJobInputResponse

Uri string: [Required] Input Asset URI.
Description string: Description for the input.
Mode string: Input Asset Delivery Mode.

Uri string: [Required] Input Asset URI.
Description string: Description for the input.
Mode string: Input Asset Delivery Mode.

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

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

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

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

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 string: [Required] Input Asset URI.
Description string: Description for the input.
Mode string: Input Asset Delivery Mode.

Uri string: [Required] Input Asset URI.
Description string: Description for the input.
Mode string: Input Asset Delivery Mode.

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

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

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

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

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 string: [Required] Input Asset URI.
Description string: Description for the input.
Mode string: Input Asset Delivery Mode.

Uri string: [Required] Input Asset URI.
Description string: Description for the input.
Mode string: Input Asset Delivery Mode.

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

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

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

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

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 v1 docs if using the v1 version of this package.

Azure Native v2.89.1 published on Sunday, Mar 2, 2025 by Pulumi

pulumi/pulumi-azure-native

azure-native.machinelearningservices.getJob

On this page

On this page

Using getJob

getJob Result

Supporting Types

AllNodesResponse

AmlTokenResponse

AutoForecastHorizonResponse

AutoMLJobResponse

AutoNCrossValidationsResponse

AutoSeasonalityResponse

AutoTargetLagsResponse

AutoTargetRollingWindowSizeResponse

BanditPolicyResponse

BayesianSamplingAlgorithmResponse

ClassificationResponse

ClassificationTrainingSettingsResponse

ColumnTransformerResponse

CommandJobLimitsResponse

CommandJobResponse

CustomForecastHorizonResponse

CustomModelJobInputResponse

CustomModelJobOutputResponse

CustomNCrossValidationsResponse

CustomSeasonalityResponse

CustomTargetLagsResponse

CustomTargetRollingWindowSizeResponse

ForecastingResponse

ForecastingSettingsResponse

ForecastingTrainingSettingsResponse

GridSamplingAlgorithmResponse

ImageClassificationMultilabelResponse

ImageClassificationResponse

ImageInstanceSegmentationResponse

ImageLimitSettingsResponse

ImageModelDistributionSettingsClassificationResponse

ImageModelDistributionSettingsObjectDetectionResponse

ImageModelSettingsClassificationResponse

ImageModelSettingsObjectDetectionResponse

ImageObjectDetectionResponse

ImageSweepSettingsResponse

JobResourceConfigurationResponse

JobServiceResponse

LiteralJobInputResponse

MLFlowModelJobInputResponse

MLFlowModelJobOutputResponse

MLTableJobInputResponse

MLTableJobOutputResponse

ManagedIdentityResponse

MedianStoppingPolicyResponse

MpiResponse

NlpVerticalFeaturizationSettingsResponse

NlpVerticalLimitSettingsResponse

ObjectiveResponse

PipelineJobResponse

PyTorchResponse

RandomSamplingAlgorithmResponse

RegressionResponse

RegressionTrainingSettingsResponse

StackEnsembleSettingsResponse

SweepJobLimitsResponse

SweepJobResponse

SystemDataResponse

TableVerticalFeaturizationSettingsResponse

TableVerticalLimitSettingsResponse

TensorFlowResponse

TextClassificationMultilabelResponse

TextClassificationResponse

TextNerResponse

TrialComponentResponse

TritonModelJobInputResponse

TritonModelJobOutputResponse

TruncationSelectionPolicyResponse

UriFileJobInputResponse

UriFileJobOutputResponse

UriFolderJobInputResponse

UriFolderJobOutputResponse

UserIdentityResponse

Package Details