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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 v2.42.1 published on Wednesday, May 22, 2024 by Pulumi

azure-native.machinelearningservices.getSchedule

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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 v2.42.1 published on Wednesday, May 22, 2024 by Pulumi

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

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

    Using getSchedule

    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 getSchedule(args: GetScheduleArgs, opts?: InvokeOptions): Promise<GetScheduleResult>
    function getScheduleOutput(args: GetScheduleOutputArgs, opts?: InvokeOptions): Output<GetScheduleResult>
    def get_schedule(name: Optional[str] = None,
                     resource_group_name: Optional[str] = None,
                     workspace_name: Optional[str] = None,
                     opts: Optional[InvokeOptions] = None) -> GetScheduleResult
    def get_schedule_output(name: 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[GetScheduleResult]
    func LookupSchedule(ctx *Context, args *LookupScheduleArgs, opts ...InvokeOption) (*LookupScheduleResult, error)
    func LookupScheduleOutput(ctx *Context, args *LookupScheduleOutputArgs, opts ...InvokeOption) LookupScheduleResultOutput

    > Note: This function is named LookupSchedule in the Go SDK.

    public static class GetSchedule 
    {
        public static Task<GetScheduleResult> InvokeAsync(GetScheduleArgs args, InvokeOptions? opts = null)
        public static Output<GetScheduleResult> Invoke(GetScheduleInvokeArgs args, InvokeOptions? opts = null)
    }
    public static CompletableFuture<GetScheduleResult> getSchedule(GetScheduleArgs args, InvokeOptions options)
    // Output-based functions aren't available in Java yet
    
    fn::invoke:
      function: azure-native:machinelearningservices:getSchedule
      arguments:
        # arguments dictionary

    The following arguments are supported:

    Name string
    Schedule name.
    ResourceGroupName string
    The name of the resource group. The name is case insensitive.
    WorkspaceName string
    Name of Azure Machine Learning workspace.
    Name string
    Schedule name.
    ResourceGroupName string
    The name of the resource group. The name is case insensitive.
    WorkspaceName string
    Name of Azure Machine Learning workspace.
    name String
    Schedule name.
    resourceGroupName String
    The name of the resource group. The name is case insensitive.
    workspaceName String
    Name of Azure Machine Learning workspace.
    name string
    Schedule name.
    resourceGroupName string
    The name of the resource group. The name is case insensitive.
    workspaceName string
    Name of Azure Machine Learning workspace.
    name str
    Schedule name.
    resource_group_name str
    The name of the resource group. The name is case insensitive.
    workspace_name str
    Name of Azure Machine Learning workspace.
    name String
    Schedule name.
    resourceGroupName String
    The name of the resource group. The name is case insensitive.
    workspaceName String
    Name of Azure Machine Learning workspace.

    getSchedule 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}
    Name string
    The name of the resource
    ScheduleProperties Pulumi.AzureNative.MachineLearningServices.Outputs.ScheduleResponse
    [Required] Additional attributes of the entity.
    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}
    Name string
    The name of the resource
    ScheduleProperties ScheduleResponse
    [Required] Additional attributes of the entity.
    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}
    name String
    The name of the resource
    scheduleProperties ScheduleResponse
    [Required] Additional attributes of the entity.
    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}
    name string
    The name of the resource
    scheduleProperties ScheduleResponse
    [Required] Additional attributes of the entity.
    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}
    name str
    The name of the resource
    schedule_properties ScheduleResponse
    [Required] Additional attributes of the entity.
    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}
    name String
    The name of the resource
    scheduleProperties Property Map
    [Required] Additional attributes of the entity.
    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.

    CronTriggerResponse

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

    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.

    EndpointScheduleActionResponse

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

    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 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).

    JobScheduleActionResponse

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

    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