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DataRobot v0.8.16 published on Tuesday, Mar 18, 2025 by DataRobot, Inc.
datarobot-community/pulumi-datarobot

datarobot.BatchPredictionJobDefinition

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DataRobot v0.8.16 published on Tuesday, Mar 18, 2025 by DataRobot, Inc.
datarobot-community/pulumi-datarobot

    Batch Prediction Job Definition

    Example Usage

    Coming soon!

    Coming soon!

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    package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.datarobot.BatchPredictionJobDefinition;
import com.pulumi.datarobot.BatchPredictionJobDefinitionArgs;
import com.pulumi.datarobot.inputs.BatchPredictionJobDefinitionIntakeSettingsArgs;
import com.pulumi.datarobot.inputs.BatchPredictionJobDefinitionOutputSettingsArgs;
import com.pulumi.datarobot.inputs.BatchPredictionJobDefinitionCsvSettingsArgs;
import com.pulumi.datarobot.inputs.BatchPredictionJobDefinitionScheduleArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var example = new BatchPredictionJobDefinition("example", BatchPredictionJobDefinitionArgs.builder()
            .deploymentId(datarobot_deployment.batch_prediction_job_definition().id())
            .intakeSettings(BatchPredictionJobDefinitionIntakeSettingsArgs.builder()
                .type("s3")
                .url("s3://datarobot-public-datasets-redistributable/1k_diabetes_simplified_features.csv")
                .credential_id(datarobot_basic_credential.batch_prediction_job_definition().id())
                .build())
            .outputSettings(BatchPredictionJobDefinitionOutputSettingsArgs.builder()
                .type("s3")
                .url("s3://my-test-bucket/predictions.csv")
                .credential_id(datarobot_basic_credential.batch_prediction_job_definition().id())
                .build())
            .csvSettings(BatchPredictionJobDefinitionCsvSettingsArgs.builder()
                .delimiter(".")
                .quotechar("'")
                .encoding("utf-8")
                .build())
            .numConcurrent(1)
            .chunkSize(10)
            .maxExplanations(5)
            .thresholdHigh(0.8)
            .thresholdLow(0.2)
            .predictionThreshold(0.5)
            .includePredictionStatus(true)
            .skipDriftTracking(true)
            .passthroughColumnsSet("all")
            .abortOnError(false)
            .includeProbabilities(true)
            .columnNamesRemapping(Map.of("col1", "newCol1"))
            .schedule(BatchPredictionJobDefinitionScheduleArgs.builder()
                .minute(                
                    "15",
                    "45")
                .hour("*")
                .month("*")
                .day_of_month("*")
                .day_of_week("*")
                .build())
            .build());

        ctx.export("exampleId", example.id());
    }
}

    resources:
  example:
    type: datarobot:BatchPredictionJobDefinition
    properties:
      deploymentId: ${datarobot_deployment.batch_prediction_job_definition.id}
      intakeSettings:
        type: s3
        url: s3://datarobot-public-datasets-redistributable/1k_diabetes_simplified_features.csv
        credential_id: ${datarobot_basic_credential.batch_prediction_job_definition.id}
      # Optional parameters
      outputSettings:
        type: s3
        url: s3://my-test-bucket/predictions.csv
        credential_id: ${datarobot_basic_credential.batch_prediction_job_definition.id}
      csvSettings:
        delimiter: .
        quotechar: ''''
        encoding: utf-8
      numConcurrent: 1
      chunkSize: 10
      maxExplanations: 5
      thresholdHigh: 0.8
      thresholdLow: 0.2
      predictionThreshold: 0.5
      includePredictionStatus: true
      skipDriftTracking: true
      passthroughColumnsSet: all
      abortOnError: false
      includeProbabilities: true
      columnNamesRemapping:
        col1: newCol1
      schedule:
        minute:
          - '15'
          - '45'
        hour:
          - '*'
        month:
          - '*'
        day_of_month:
          - '*'
        day_of_week:
          - '*'
outputs:
  exampleId: ${example.id}

    Create BatchPredictionJobDefinition Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new BatchPredictionJobDefinition(name: string, args: BatchPredictionJobDefinitionArgs, opts?: CustomResourceOptions);
    @overload
def BatchPredictionJobDefinition(resource_name: str,
                                 args: BatchPredictionJobDefinitionArgs,
                                 opts: Optional[ResourceOptions] = None)

@overload
def BatchPredictionJobDefinition(resource_name: str,
                                 opts: Optional[ResourceOptions] = None,
                                 deployment_id: Optional[str] = None,
                                 intake_settings: Optional[BatchPredictionJobDefinitionIntakeSettingsArgs] = None,
                                 name: Optional[str] = None,
                                 timeseries_settings: Optional[BatchPredictionJobDefinitionTimeseriesSettingsArgs] = None,
                                 abort_on_error: Optional[bool] = None,
                                 enabled: Optional[bool] = None,
                                 explanation_algorithm: Optional[str] = None,
                                 include_prediction_status: Optional[bool] = None,
                                 include_probabilities: Optional[bool] = None,
                                 include_probabilities_classes: Optional[Sequence[str]] = None,
                                 chunk_size: Optional[Any] = None,
                                 output_settings: Optional[BatchPredictionJobDefinitionOutputSettingsArgs] = None,
                                 column_names_remapping: Optional[Mapping[str, str]] = None,
                                 csv_settings: Optional[BatchPredictionJobDefinitionCsvSettingsArgs] = None,
                                 max_explanations: Optional[int] = None,
                                 passthrough_columns: Optional[Sequence[str]] = None,
                                 passthrough_columns_set: Optional[str] = None,
                                 prediction_instance: Optional[BatchPredictionJobDefinitionPredictionInstanceArgs] = None,
                                 prediction_threshold: Optional[float] = None,
                                 prediction_warning_enabled: Optional[bool] = None,
                                 schedule: Optional[BatchPredictionJobDefinitionScheduleArgs] = None,
                                 skip_drift_tracking: Optional[bool] = None,
                                 threshold_high: Optional[float] = None,
                                 threshold_low: Optional[float] = None,
                                 num_concurrent: Optional[int] = None)
    func NewBatchPredictionJobDefinition(ctx *Context, name string, args BatchPredictionJobDefinitionArgs, opts ...ResourceOption) (*BatchPredictionJobDefinition, error)
    public BatchPredictionJobDefinition(string name, BatchPredictionJobDefinitionArgs args, CustomResourceOptions? opts = null)
    public BatchPredictionJobDefinition(String name, BatchPredictionJobDefinitionArgs args)
public BatchPredictionJobDefinition(String name, BatchPredictionJobDefinitionArgs args, CustomResourceOptions options)

    type: datarobot:BatchPredictionJobDefinition
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

    Parameters

    name string
    The unique name of the resource.
    args BatchPredictionJobDefinitionArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args BatchPredictionJobDefinitionArgs
    The arguments to resource properties.
    opts ResourceOptions
    Bag of options to control resource's behavior.
    ctx Context
    Context object for the current deployment.
    name string
    The unique name of the resource.
    args BatchPredictionJobDefinitionArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args BatchPredictionJobDefinitionArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args BatchPredictionJobDefinitionArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Constructor example

    The following reference example uses placeholder values for all input properties.

    var batchPredictionJobDefinitionResource = new Datarobot.BatchPredictionJobDefinition("batchPredictionJobDefinitionResource", new()
{
    DeploymentId = "string",
    IntakeSettings = new Datarobot.Inputs.BatchPredictionJobDefinitionIntakeSettingsArgs
    {
        Type = "string",
        Catalog = "string",
        CredentialId = "string",
        DataStoreId = "string",
        DatasetId = "string",
        EndpointUrl = "string",
        FetchSize = 0,
        File = "string",
        Query = "string",
        Schema = "string",
        Table = "string",
        Url = "string",
    },
    Name = "string",
    TimeseriesSettings = new Datarobot.Inputs.BatchPredictionJobDefinitionTimeseriesSettingsArgs
    {
        ForecastPoint = "string",
        PredictionsEndDate = "string",
        PredictionsStartDate = "string",
        RelaxKnownInAdvanceFeaturesCheck = false,
        Type = "string",
    },
    AbortOnError = false,
    Enabled = false,
    ExplanationAlgorithm = "string",
    IncludePredictionStatus = false,
    IncludeProbabilities = false,
    IncludeProbabilitiesClasses = new[]
    {
        "string",
    },
    ChunkSize = "any",
    OutputSettings = new Datarobot.Inputs.BatchPredictionJobDefinitionOutputSettingsArgs
    {
        Catalog = "string",
        CreateTableIfNotExists = false,
        CredentialId = "string",
        DataStoreId = "string",
        EndpointUrl = "string",
        Path = "string",
        Schema = "string",
        StatementType = "string",
        Table = "string",
        Type = "string",
        UpdateColumns = new[]
        {
            "string",
        },
        Url = "string",
        WhereColumns = new[]
        {
            "string",
        },
    },
    ColumnNamesRemapping = 
    {
        { "string", "string" },
    },
    CsvSettings = new Datarobot.Inputs.BatchPredictionJobDefinitionCsvSettingsArgs
    {
        Delimiter = "string",
        Encoding = "string",
        Quotechar = "string",
    },
    MaxExplanations = 0,
    PassthroughColumns = new[]
    {
        "string",
    },
    PassthroughColumnsSet = "string",
    PredictionInstance = new Datarobot.Inputs.BatchPredictionJobDefinitionPredictionInstanceArgs
    {
        HostName = "string",
        ApiKey = "string",
        DatarobotKey = "string",
        SslEnabled = false,
    },
    PredictionThreshold = 0,
    PredictionWarningEnabled = false,
    Schedule = new Datarobot.Inputs.BatchPredictionJobDefinitionScheduleArgs
    {
        DayOfMonths = new[]
        {
            "string",
        },
        DayOfWeeks = new[]
        {
            "string",
        },
        Hours = new[]
        {
            "string",
        },
        Minutes = new[]
        {
            "string",
        },
        Months = new[]
        {
            "string",
        },
    },
    SkipDriftTracking = false,
    ThresholdHigh = 0,
    ThresholdLow = 0,
    NumConcurrent = 0,
});

    example, err := datarobot.NewBatchPredictionJobDefinition(ctx, "batchPredictionJobDefinitionResource", &datarobot.BatchPredictionJobDefinitionArgs{
	DeploymentId: pulumi.String("string"),
	IntakeSettings: &datarobot.BatchPredictionJobDefinitionIntakeSettingsArgs{
		Type:         pulumi.String("string"),
		Catalog:      pulumi.String("string"),
		CredentialId: pulumi.String("string"),
		DataStoreId:  pulumi.String("string"),
		DatasetId:    pulumi.String("string"),
		EndpointUrl:  pulumi.String("string"),
		FetchSize:    pulumi.Int(0),
		File:         pulumi.String("string"),
		Query:        pulumi.String("string"),
		Schema:       pulumi.String("string"),
		Table:        pulumi.String("string"),
		Url:          pulumi.String("string"),
	},
	Name: pulumi.String("string"),
	TimeseriesSettings: &datarobot.BatchPredictionJobDefinitionTimeseriesSettingsArgs{
		ForecastPoint:                    pulumi.String("string"),
		PredictionsEndDate:               pulumi.String("string"),
		PredictionsStartDate:             pulumi.String("string"),
		RelaxKnownInAdvanceFeaturesCheck: pulumi.Bool(false),
		Type:                             pulumi.String("string"),
	},
	AbortOnError:            pulumi.Bool(false),
	Enabled:                 pulumi.Bool(false),
	ExplanationAlgorithm:    pulumi.String("string"),
	IncludePredictionStatus: pulumi.Bool(false),
	IncludeProbabilities:    pulumi.Bool(false),
	IncludeProbabilitiesClasses: pulumi.StringArray{
		pulumi.String("string"),
	},
	ChunkSize: pulumi.Any("any"),
	OutputSettings: &datarobot.BatchPredictionJobDefinitionOutputSettingsArgs{
		Catalog:                pulumi.String("string"),
		CreateTableIfNotExists: pulumi.Bool(false),
		CredentialId:           pulumi.String("string"),
		DataStoreId:            pulumi.String("string"),
		EndpointUrl:            pulumi.String("string"),
		Path:                   pulumi.String("string"),
		Schema:                 pulumi.String("string"),
		StatementType:          pulumi.String("string"),
		Table:                  pulumi.String("string"),
		Type:                   pulumi.String("string"),
		UpdateColumns: pulumi.StringArray{
			pulumi.String("string"),
		},
		Url: pulumi.String("string"),
		WhereColumns: pulumi.StringArray{
			pulumi.String("string"),
		},
	},
	ColumnNamesRemapping: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	CsvSettings: &datarobot.BatchPredictionJobDefinitionCsvSettingsArgs{
		Delimiter: pulumi.String("string"),
		Encoding:  pulumi.String("string"),
		Quotechar: pulumi.String("string"),
	},
	MaxExplanations: pulumi.Int(0),
	PassthroughColumns: pulumi.StringArray{
		pulumi.String("string"),
	},
	PassthroughColumnsSet: pulumi.String("string"),
	PredictionInstance: &datarobot.BatchPredictionJobDefinitionPredictionInstanceArgs{
		HostName:     pulumi.String("string"),
		ApiKey:       pulumi.String("string"),
		DatarobotKey: pulumi.String("string"),
		SslEnabled:   pulumi.Bool(false),
	},
	PredictionThreshold:      pulumi.Float64(0),
	PredictionWarningEnabled: pulumi.Bool(false),
	Schedule: &datarobot.BatchPredictionJobDefinitionScheduleArgs{
		DayOfMonths: pulumi.StringArray{
			pulumi.String("string"),
		},
		DayOfWeeks: pulumi.StringArray{
			pulumi.String("string"),
		},
		Hours: pulumi.StringArray{
			pulumi.String("string"),
		},
		Minutes: pulumi.StringArray{
			pulumi.String("string"),
		},
		Months: pulumi.StringArray{
			pulumi.String("string"),
		},
	},
	SkipDriftTracking: pulumi.Bool(false),
	ThresholdHigh:     pulumi.Float64(0),
	ThresholdLow:      pulumi.Float64(0),
	NumConcurrent:     pulumi.Int(0),
})

    var batchPredictionJobDefinitionResource = new BatchPredictionJobDefinition("batchPredictionJobDefinitionResource", BatchPredictionJobDefinitionArgs.builder()
    .deploymentId("string")
    .intakeSettings(BatchPredictionJobDefinitionIntakeSettingsArgs.builder()
        .type("string")
        .catalog("string")
        .credentialId("string")
        .dataStoreId("string")
        .datasetId("string")
        .endpointUrl("string")
        .fetchSize(0)
        .file("string")
        .query("string")
        .schema("string")
        .table("string")
        .url("string")
        .build())
    .name("string")
    .timeseriesSettings(BatchPredictionJobDefinitionTimeseriesSettingsArgs.builder()
        .forecastPoint("string")
        .predictionsEndDate("string")
        .predictionsStartDate("string")
        .relaxKnownInAdvanceFeaturesCheck(false)
        .type("string")
        .build())
    .abortOnError(false)
    .enabled(false)
    .explanationAlgorithm("string")
    .includePredictionStatus(false)
    .includeProbabilities(false)
    .includeProbabilitiesClasses("string")
    .chunkSize("any")
    .outputSettings(BatchPredictionJobDefinitionOutputSettingsArgs.builder()
        .catalog("string")
        .createTableIfNotExists(false)
        .credentialId("string")
        .dataStoreId("string")
        .endpointUrl("string")
        .path("string")
        .schema("string")
        .statementType("string")
        .table("string")
        .type("string")
        .updateColumns("string")
        .url("string")
        .whereColumns("string")
        .build())
    .columnNamesRemapping(Map.of("string", "string"))
    .csvSettings(BatchPredictionJobDefinitionCsvSettingsArgs.builder()
        .delimiter("string")
        .encoding("string")
        .quotechar("string")
        .build())
    .maxExplanations(0)
    .passthroughColumns("string")
    .passthroughColumnsSet("string")
    .predictionInstance(BatchPredictionJobDefinitionPredictionInstanceArgs.builder()
        .hostName("string")
        .apiKey("string")
        .datarobotKey("string")
        .sslEnabled(false)
        .build())
    .predictionThreshold(0)
    .predictionWarningEnabled(false)
    .schedule(BatchPredictionJobDefinitionScheduleArgs.builder()
        .dayOfMonths("string")
        .dayOfWeeks("string")
        .hours("string")
        .minutes("string")
        .months("string")
        .build())
    .skipDriftTracking(false)
    .thresholdHigh(0)
    .thresholdLow(0)
    .numConcurrent(0)
    .build());

    batch_prediction_job_definition_resource = datarobot.BatchPredictionJobDefinition("batchPredictionJobDefinitionResource",
    deployment_id="string",
    intake_settings={
        "type": "string",
        "catalog": "string",
        "credential_id": "string",
        "data_store_id": "string",
        "dataset_id": "string",
        "endpoint_url": "string",
        "fetch_size": 0,
        "file": "string",
        "query": "string",
        "schema": "string",
        "table": "string",
        "url": "string",
    },
    name="string",
    timeseries_settings={
        "forecast_point": "string",
        "predictions_end_date": "string",
        "predictions_start_date": "string",
        "relax_known_in_advance_features_check": False,
        "type": "string",
    },
    abort_on_error=False,
    enabled=False,
    explanation_algorithm="string",
    include_prediction_status=False,
    include_probabilities=False,
    include_probabilities_classes=["string"],
    chunk_size="any",
    output_settings={
        "catalog": "string",
        "create_table_if_not_exists": False,
        "credential_id": "string",
        "data_store_id": "string",
        "endpoint_url": "string",
        "path": "string",
        "schema": "string",
        "statement_type": "string",
        "table": "string",
        "type": "string",
        "update_columns": ["string"],
        "url": "string",
        "where_columns": ["string"],
    },
    column_names_remapping={
        "string": "string",
    },
    csv_settings={
        "delimiter": "string",
        "encoding": "string",
        "quotechar": "string",
    },
    max_explanations=0,
    passthrough_columns=["string"],
    passthrough_columns_set="string",
    prediction_instance={
        "host_name": "string",
        "api_key": "string",
        "datarobot_key": "string",
        "ssl_enabled": False,
    },
    prediction_threshold=0,
    prediction_warning_enabled=False,
    schedule={
        "day_of_months": ["string"],
        "day_of_weeks": ["string"],
        "hours": ["string"],
        "minutes": ["string"],
        "months": ["string"],
    },
    skip_drift_tracking=False,
    threshold_high=0,
    threshold_low=0,
    num_concurrent=0)

    const batchPredictionJobDefinitionResource = new datarobot.BatchPredictionJobDefinition("batchPredictionJobDefinitionResource", {
    deploymentId: "string",
    intakeSettings: {
        type: "string",
        catalog: "string",
        credentialId: "string",
        dataStoreId: "string",
        datasetId: "string",
        endpointUrl: "string",
        fetchSize: 0,
        file: "string",
        query: "string",
        schema: "string",
        table: "string",
        url: "string",
    },
    name: "string",
    timeseriesSettings: {
        forecastPoint: "string",
        predictionsEndDate: "string",
        predictionsStartDate: "string",
        relaxKnownInAdvanceFeaturesCheck: false,
        type: "string",
    },
    abortOnError: false,
    enabled: false,
    explanationAlgorithm: "string",
    includePredictionStatus: false,
    includeProbabilities: false,
    includeProbabilitiesClasses: ["string"],
    chunkSize: "any",
    outputSettings: {
        catalog: "string",
        createTableIfNotExists: false,
        credentialId: "string",
        dataStoreId: "string",
        endpointUrl: "string",
        path: "string",
        schema: "string",
        statementType: "string",
        table: "string",
        type: "string",
        updateColumns: ["string"],
        url: "string",
        whereColumns: ["string"],
    },
    columnNamesRemapping: {
        string: "string",
    },
    csvSettings: {
        delimiter: "string",
        encoding: "string",
        quotechar: "string",
    },
    maxExplanations: 0,
    passthroughColumns: ["string"],
    passthroughColumnsSet: "string",
    predictionInstance: {
        hostName: "string",
        apiKey: "string",
        datarobotKey: "string",
        sslEnabled: false,
    },
    predictionThreshold: 0,
    predictionWarningEnabled: false,
    schedule: {
        dayOfMonths: ["string"],
        dayOfWeeks: ["string"],
        hours: ["string"],
        minutes: ["string"],
        months: ["string"],
    },
    skipDriftTracking: false,
    thresholdHigh: 0,
    thresholdLow: 0,
    numConcurrent: 0,
});

    type: datarobot:BatchPredictionJobDefinition
properties:
    abortOnError: false
    chunkSize: any
    columnNamesRemapping:
        string: string
    csvSettings:
        delimiter: string
        encoding: string
        quotechar: string
    deploymentId: string
    enabled: false
    explanationAlgorithm: string
    includePredictionStatus: false
    includeProbabilities: false
    includeProbabilitiesClasses:
        - string
    intakeSettings:
        catalog: string
        credentialId: string
        dataStoreId: string
        datasetId: string
        endpointUrl: string
        fetchSize: 0
        file: string
        query: string
        schema: string
        table: string
        type: string
        url: string
    maxExplanations: 0
    name: string
    numConcurrent: 0
    outputSettings:
        catalog: string
        createTableIfNotExists: false
        credentialId: string
        dataStoreId: string
        endpointUrl: string
        path: string
        schema: string
        statementType: string
        table: string
        type: string
        updateColumns:
            - string
        url: string
        whereColumns:
            - string
    passthroughColumns:
        - string
    passthroughColumnsSet: string
    predictionInstance:
        apiKey: string
        datarobotKey: string
        hostName: string
        sslEnabled: false
    predictionThreshold: 0
    predictionWarningEnabled: false
    schedule:
        dayOfMonths:
            - string
        dayOfWeeks:
            - string
        hours:
            - string
        minutes:
            - string
        months:
            - string
    skipDriftTracking: false
    thresholdHigh: 0
    thresholdLow: 0
    timeseriesSettings:
        forecastPoint: string
        predictionsEndDate: string
        predictionsStartDate: string
        relaxKnownInAdvanceFeaturesCheck: false
        type: string

    BatchPredictionJobDefinition Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

    The BatchPredictionJobDefinition resource accepts the following input properties:

    DeploymentId string
    The ID of the deployment to use for the batch prediction job.
    IntakeSettings DataRobotBatchPredictionJobDefinitionIntakeSettings
    A dict configuring how data is coming from.
    AbortOnError bool
    Default behavior is to abort the job if too many rows fail scoring. This will free up resources for other jobs that may score successfully. Set to false to unconditionally score every row no matter how many errors are encountered. Defaults to True.
    ChunkSize object
    Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.
    ColumnNamesRemapping Dictionary<string, string>
    Mapping with column renaming for output table.
    CsvSettings DataRobotBatchPredictionJobDefinitionCsvSettings
    CSV intake and output settings.
    Enabled bool
    Whether or not the job definition should be active on a scheduled basis. If True, schedule is required.
    ExplanationAlgorithm string
    Which algorithm will be used to calculate prediction explanations.
    IncludePredictionStatus bool
    Include the prediction_status column in the output. Defaults to False.
    IncludeProbabilities bool
    Flag that enables returning of all probability columns. Defaults to True.
    IncludeProbabilitiesClasses List<string>
    List the subset of classes if a user doesn’t want all the classes. Defaults to [].
    MaxExplanations int
    Compute prediction explanations for this amount of features.
    Name string
    The name you want your job to be identified with. Must be unique across the organization’s existing jobs.
    NumConcurrent int
    Number of concurrent chunks to score simultaneously. Defaults to the available number of cores of the deployment. Lower it to leave resources for real-time scoring.
    OutputSettings DataRobotBatchPredictionJobDefinitionOutputSettings
    A dict configuring how scored data is to be saved.
    PassthroughColumns List<string>
    Keep these columns from the scoring dataset in the scored dataset. This is useful for correlating predictions with source data.
    PassthroughColumnsSet string
    To pass through every column from the scoring dataset, set this to all.
    PredictionInstance DataRobotBatchPredictionJobDefinitionPredictionInstance
    Defaults to instance specified by deployment or system configuration.
    PredictionThreshold double
    Threshold is the point that sets the class boundary for a predicted value. This value can be set between 0.0 and 1.0.
    PredictionWarningEnabled bool
    Add prediction warnings to the scored data. Currently only supported for regression models. Defaults to False.
    Schedule DataRobotBatchPredictionJobDefinitionSchedule
    Defines at what intervals the job should run.
    SkipDriftTracking bool
    Skips drift tracking on any predictions made from this job. This is useful when running non-production workloads to not affect drift tracking and cause unnecessary alerts. Defaults to false.
    ThresholdHigh double
    Only compute prediction explanations for predictions above this threshold. Can be combined with threshold_low.
    ThresholdLow double
    Only compute prediction explanations for predictions below this threshold. Can be combined with threshold_high.
    TimeseriesSettings DataRobotBatchPredictionJobDefinitionTimeseriesSettings
    Configuration for time-series scoring.
    DeploymentId string
    The ID of the deployment to use for the batch prediction job.
    IntakeSettings BatchPredictionJobDefinitionIntakeSettingsArgs
    A dict configuring how data is coming from.
    AbortOnError bool
    Default behavior is to abort the job if too many rows fail scoring. This will free up resources for other jobs that may score successfully. Set to false to unconditionally score every row no matter how many errors are encountered. Defaults to True.
    ChunkSize interface{}
    Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.
    ColumnNamesRemapping map[string]string
    Mapping with column renaming for output table.
    CsvSettings BatchPredictionJobDefinitionCsvSettingsArgs
    CSV intake and output settings.
    Enabled bool
    Whether or not the job definition should be active on a scheduled basis. If True, schedule is required.
    ExplanationAlgorithm string
    Which algorithm will be used to calculate prediction explanations.
    IncludePredictionStatus bool
    Include the prediction_status column in the output. Defaults to False.
    IncludeProbabilities bool
    Flag that enables returning of all probability columns. Defaults to True.
    IncludeProbabilitiesClasses []string
    List the subset of classes if a user doesn’t want all the classes. Defaults to [].
    MaxExplanations int
    Compute prediction explanations for this amount of features.
    Name string
    The name you want your job to be identified with. Must be unique across the organization’s existing jobs.
    NumConcurrent int
    Number of concurrent chunks to score simultaneously. Defaults to the available number of cores of the deployment. Lower it to leave resources for real-time scoring.
    OutputSettings BatchPredictionJobDefinitionOutputSettingsArgs
    A dict configuring how scored data is to be saved.
    PassthroughColumns []string
    Keep these columns from the scoring dataset in the scored dataset. This is useful for correlating predictions with source data.
    PassthroughColumnsSet string
    To pass through every column from the scoring dataset, set this to all.
    PredictionInstance BatchPredictionJobDefinitionPredictionInstanceArgs
    Defaults to instance specified by deployment or system configuration.
    PredictionThreshold float64
    Threshold is the point that sets the class boundary for a predicted value. This value can be set between 0.0 and 1.0.
    PredictionWarningEnabled bool
    Add prediction warnings to the scored data. Currently only supported for regression models. Defaults to False.
    Schedule BatchPredictionJobDefinitionScheduleArgs
    Defines at what intervals the job should run.
    SkipDriftTracking bool
    Skips drift tracking on any predictions made from this job. This is useful when running non-production workloads to not affect drift tracking and cause unnecessary alerts. Defaults to false.
    ThresholdHigh float64
    Only compute prediction explanations for predictions above this threshold. Can be combined with threshold_low.
    ThresholdLow float64
    Only compute prediction explanations for predictions below this threshold. Can be combined with threshold_high.
    TimeseriesSettings BatchPredictionJobDefinitionTimeseriesSettingsArgs
    Configuration for time-series scoring.
    deploymentId String
    The ID of the deployment to use for the batch prediction job.
    intakeSettings BatchPredictionJobDefinitionIntakeSettings
    A dict configuring how data is coming from.
    abortOnError Boolean
    Default behavior is to abort the job if too many rows fail scoring. This will free up resources for other jobs that may score successfully. Set to false to unconditionally score every row no matter how many errors are encountered. Defaults to True.
    chunkSize Object
    Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.
    columnNamesRemapping Map<String,String>
    Mapping with column renaming for output table.
    csvSettings BatchPredictionJobDefinitionCsvSettings
    CSV intake and output settings.
    enabled Boolean
    Whether or not the job definition should be active on a scheduled basis. If True, schedule is required.
    explanationAlgorithm String
    Which algorithm will be used to calculate prediction explanations.
    includePredictionStatus Boolean
    Include the prediction_status column in the output. Defaults to False.
    includeProbabilities Boolean
    Flag that enables returning of all probability columns. Defaults to True.
    includeProbabilitiesClasses List<String>
    List the subset of classes if a user doesn’t want all the classes. Defaults to [].
    maxExplanations Integer
    Compute prediction explanations for this amount of features.
    name String
    The name you want your job to be identified with. Must be unique across the organization’s existing jobs.
    numConcurrent Integer
    Number of concurrent chunks to score simultaneously. Defaults to the available number of cores of the deployment. Lower it to leave resources for real-time scoring.
    outputSettings BatchPredictionJobDefinitionOutputSettings
    A dict configuring how scored data is to be saved.
    passthroughColumns List<String>
    Keep these columns from the scoring dataset in the scored dataset. This is useful for correlating predictions with source data.
    passthroughColumnsSet String
    To pass through every column from the scoring dataset, set this to all.
    predictionInstance BatchPredictionJobDefinitionPredictionInstance
    Defaults to instance specified by deployment or system configuration.
    predictionThreshold Double
    Threshold is the point that sets the class boundary for a predicted value. This value can be set between 0.0 and 1.0.
    predictionWarningEnabled Boolean
    Add prediction warnings to the scored data. Currently only supported for regression models. Defaults to False.
    schedule BatchPredictionJobDefinitionSchedule
    Defines at what intervals the job should run.
    skipDriftTracking Boolean
    Skips drift tracking on any predictions made from this job. This is useful when running non-production workloads to not affect drift tracking and cause unnecessary alerts. Defaults to false.
    thresholdHigh Double
    Only compute prediction explanations for predictions above this threshold. Can be combined with threshold_low.
    thresholdLow Double
    Only compute prediction explanations for predictions below this threshold. Can be combined with threshold_high.
    timeseriesSettings BatchPredictionJobDefinitionTimeseriesSettings
    Configuration for time-series scoring.
    deploymentId string
    The ID of the deployment to use for the batch prediction job.
    intakeSettings BatchPredictionJobDefinitionIntakeSettings
    A dict configuring how data is coming from.
    abortOnError boolean
    Default behavior is to abort the job if too many rows fail scoring. This will free up resources for other jobs that may score successfully. Set to false to unconditionally score every row no matter how many errors are encountered. Defaults to True.
    chunkSize any
    Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.
    columnNamesRemapping {[key: string]: string}
    Mapping with column renaming for output table.
    csvSettings BatchPredictionJobDefinitionCsvSettings
    CSV intake and output settings.
    enabled boolean
    Whether or not the job definition should be active on a scheduled basis. If True, schedule is required.
    explanationAlgorithm string
    Which algorithm will be used to calculate prediction explanations.
    includePredictionStatus boolean
    Include the prediction_status column in the output. Defaults to False.
    includeProbabilities boolean
    Flag that enables returning of all probability columns. Defaults to True.
    includeProbabilitiesClasses string[]
    List the subset of classes if a user doesn’t want all the classes. Defaults to [].
    maxExplanations number
    Compute prediction explanations for this amount of features.
    name string
    The name you want your job to be identified with. Must be unique across the organization’s existing jobs.
    numConcurrent number
    Number of concurrent chunks to score simultaneously. Defaults to the available number of cores of the deployment. Lower it to leave resources for real-time scoring.
    outputSettings BatchPredictionJobDefinitionOutputSettings
    A dict configuring how scored data is to be saved.
    passthroughColumns string[]
    Keep these columns from the scoring dataset in the scored dataset. This is useful for correlating predictions with source data.
    passthroughColumnsSet string
    To pass through every column from the scoring dataset, set this to all.
    predictionInstance BatchPredictionJobDefinitionPredictionInstance
    Defaults to instance specified by deployment or system configuration.
    predictionThreshold number
    Threshold is the point that sets the class boundary for a predicted value. This value can be set between 0.0 and 1.0.
    predictionWarningEnabled boolean
    Add prediction warnings to the scored data. Currently only supported for regression models. Defaults to False.
    schedule BatchPredictionJobDefinitionSchedule
    Defines at what intervals the job should run.
    skipDriftTracking boolean
    Skips drift tracking on any predictions made from this job. This is useful when running non-production workloads to not affect drift tracking and cause unnecessary alerts. Defaults to false.
    thresholdHigh number
    Only compute prediction explanations for predictions above this threshold. Can be combined with threshold_low.
    thresholdLow number
    Only compute prediction explanations for predictions below this threshold. Can be combined with threshold_high.
    timeseriesSettings BatchPredictionJobDefinitionTimeseriesSettings
    Configuration for time-series scoring.
    deployment_id str
    The ID of the deployment to use for the batch prediction job.
    intake_settings BatchPredictionJobDefinitionIntakeSettingsArgs
    A dict configuring how data is coming from.
    abort_on_error bool
    Default behavior is to abort the job if too many rows fail scoring. This will free up resources for other jobs that may score successfully. Set to false to unconditionally score every row no matter how many errors are encountered. Defaults to True.
    chunk_size Any
    Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.
    column_names_remapping Mapping[str, str]
    Mapping with column renaming for output table.
    csv_settings BatchPredictionJobDefinitionCsvSettingsArgs
    CSV intake and output settings.
    enabled bool
    Whether or not the job definition should be active on a scheduled basis. If True, schedule is required.
    explanation_algorithm str
    Which algorithm will be used to calculate prediction explanations.
    include_prediction_status bool
    Include the prediction_status column in the output. Defaults to False.
    include_probabilities bool
    Flag that enables returning of all probability columns. Defaults to True.
    include_probabilities_classes Sequence[str]
    List the subset of classes if a user doesn’t want all the classes. Defaults to [].
    max_explanations int
    Compute prediction explanations for this amount of features.
    name str
    The name you want your job to be identified with. Must be unique across the organization’s existing jobs.
    num_concurrent int
    Number of concurrent chunks to score simultaneously. Defaults to the available number of cores of the deployment. Lower it to leave resources for real-time scoring.
    output_settings BatchPredictionJobDefinitionOutputSettingsArgs
    A dict configuring how scored data is to be saved.
    passthrough_columns Sequence[str]
    Keep these columns from the scoring dataset in the scored dataset. This is useful for correlating predictions with source data.
    passthrough_columns_set str
    To pass through every column from the scoring dataset, set this to all.
    prediction_instance BatchPredictionJobDefinitionPredictionInstanceArgs
    Defaults to instance specified by deployment or system configuration.
    prediction_threshold float
    Threshold is the point that sets the class boundary for a predicted value. This value can be set between 0.0 and 1.0.
    prediction_warning_enabled bool
    Add prediction warnings to the scored data. Currently only supported for regression models. Defaults to False.
    schedule BatchPredictionJobDefinitionScheduleArgs
    Defines at what intervals the job should run.
    skip_drift_tracking bool
    Skips drift tracking on any predictions made from this job. This is useful when running non-production workloads to not affect drift tracking and cause unnecessary alerts. Defaults to false.
    threshold_high float
    Only compute prediction explanations for predictions above this threshold. Can be combined with threshold_low.
    threshold_low float
    Only compute prediction explanations for predictions below this threshold. Can be combined with threshold_high.
    timeseries_settings BatchPredictionJobDefinitionTimeseriesSettingsArgs
    Configuration for time-series scoring.
    deploymentId String
    The ID of the deployment to use for the batch prediction job.
    intakeSettings Property Map
    A dict configuring how data is coming from.
    abortOnError Boolean
    Default behavior is to abort the job if too many rows fail scoring. This will free up resources for other jobs that may score successfully. Set to false to unconditionally score every row no matter how many errors are encountered. Defaults to True.
    chunkSize Any
    Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.
    columnNamesRemapping Map<String>
    Mapping with column renaming for output table.
    csvSettings Property Map
    CSV intake and output settings.
    enabled Boolean
    Whether or not the job definition should be active on a scheduled basis. If True, schedule is required.
    explanationAlgorithm String
    Which algorithm will be used to calculate prediction explanations.
    includePredictionStatus Boolean
    Include the prediction_status column in the output. Defaults to False.
    includeProbabilities Boolean
    Flag that enables returning of all probability columns. Defaults to True.
    includeProbabilitiesClasses List<String>
    List the subset of classes if a user doesn’t want all the classes. Defaults to [].
    maxExplanations Number
    Compute prediction explanations for this amount of features.
    name String
    The name you want your job to be identified with. Must be unique across the organization’s existing jobs.
    numConcurrent Number
    Number of concurrent chunks to score simultaneously. Defaults to the available number of cores of the deployment. Lower it to leave resources for real-time scoring.
    outputSettings Property Map
    A dict configuring how scored data is to be saved.
    passthroughColumns List<String>
    Keep these columns from the scoring dataset in the scored dataset. This is useful for correlating predictions with source data.
    passthroughColumnsSet String
    To pass through every column from the scoring dataset, set this to all.
    predictionInstance Property Map
    Defaults to instance specified by deployment or system configuration.
    predictionThreshold Number
    Threshold is the point that sets the class boundary for a predicted value. This value can be set between 0.0 and 1.0.
    predictionWarningEnabled Boolean
    Add prediction warnings to the scored data. Currently only supported for regression models. Defaults to False.
    schedule Property Map
    Defines at what intervals the job should run.
    skipDriftTracking Boolean
    Skips drift tracking on any predictions made from this job. This is useful when running non-production workloads to not affect drift tracking and cause unnecessary alerts. Defaults to false.
    thresholdHigh Number
    Only compute prediction explanations for predictions above this threshold. Can be combined with threshold_low.
    thresholdLow Number
    Only compute prediction explanations for predictions below this threshold. Can be combined with threshold_high.
    timeseriesSettings Property Map
    Configuration for time-series scoring.

    Outputs

    All input properties are implicitly available as output properties. Additionally, the BatchPredictionJobDefinition resource produces the following output properties:

    Id string
    The provider-assigned unique ID for this managed resource.
    Id string
    The provider-assigned unique ID for this managed resource.
    id String
    The provider-assigned unique ID for this managed resource.
    id string
    The provider-assigned unique ID for this managed resource.
    id str
    The provider-assigned unique ID for this managed resource.
    id String
    The provider-assigned unique ID for this managed resource.

    Look up Existing BatchPredictionJobDefinition Resource

    Get an existing BatchPredictionJobDefinition resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.

    public static get(name: string, id: Input<ID>, state?: BatchPredictionJobDefinitionState, opts?: CustomResourceOptions): BatchPredictionJobDefinition
    @staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        abort_on_error: Optional[bool] = None,
        chunk_size: Optional[Any] = None,
        column_names_remapping: Optional[Mapping[str, str]] = None,
        csv_settings: Optional[BatchPredictionJobDefinitionCsvSettingsArgs] = None,
        deployment_id: Optional[str] = None,
        enabled: Optional[bool] = None,
        explanation_algorithm: Optional[str] = None,
        include_prediction_status: Optional[bool] = None,
        include_probabilities: Optional[bool] = None,
        include_probabilities_classes: Optional[Sequence[str]] = None,
        intake_settings: Optional[BatchPredictionJobDefinitionIntakeSettingsArgs] = None,
        max_explanations: Optional[int] = None,
        name: Optional[str] = None,
        num_concurrent: Optional[int] = None,
        output_settings: Optional[BatchPredictionJobDefinitionOutputSettingsArgs] = None,
        passthrough_columns: Optional[Sequence[str]] = None,
        passthrough_columns_set: Optional[str] = None,
        prediction_instance: Optional[BatchPredictionJobDefinitionPredictionInstanceArgs] = None,
        prediction_threshold: Optional[float] = None,
        prediction_warning_enabled: Optional[bool] = None,
        schedule: Optional[BatchPredictionJobDefinitionScheduleArgs] = None,
        skip_drift_tracking: Optional[bool] = None,
        threshold_high: Optional[float] = None,
        threshold_low: Optional[float] = None,
        timeseries_settings: Optional[BatchPredictionJobDefinitionTimeseriesSettingsArgs] = None) -> BatchPredictionJobDefinition
    func GetBatchPredictionJobDefinition(ctx *Context, name string, id IDInput, state *BatchPredictionJobDefinitionState, opts ...ResourceOption) (*BatchPredictionJobDefinition, error)
    public static BatchPredictionJobDefinition Get(string name, Input<string> id, BatchPredictionJobDefinitionState? state, CustomResourceOptions? opts = null)
    public static BatchPredictionJobDefinition get(String name, Output<String> id, BatchPredictionJobDefinitionState state, CustomResourceOptions options)
    resources:  _:    type: datarobot:BatchPredictionJobDefinition    get:      id: ${id}
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    resource_name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    name
    The unique name of the resulting resource.
    id
    The unique provider ID of the resource to lookup.
    state
    Any extra arguments used during the lookup.
    opts
    A bag of options that control this resource's behavior.
    The following state arguments are supported:
    AbortOnError bool
    Default behavior is to abort the job if too many rows fail scoring. This will free up resources for other jobs that may score successfully. Set to false to unconditionally score every row no matter how many errors are encountered. Defaults to True.
    ChunkSize object
    Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.
    ColumnNamesRemapping Dictionary<string, string>
    Mapping with column renaming for output table.
    CsvSettings DataRobotBatchPredictionJobDefinitionCsvSettings
    CSV intake and output settings.
    DeploymentId string
    The ID of the deployment to use for the batch prediction job.
    Enabled bool
    Whether or not the job definition should be active on a scheduled basis. If True, schedule is required.
    ExplanationAlgorithm string
    Which algorithm will be used to calculate prediction explanations.
    IncludePredictionStatus bool
    Include the prediction_status column in the output. Defaults to False.
    IncludeProbabilities bool
    Flag that enables returning of all probability columns. Defaults to True.
    IncludeProbabilitiesClasses List<string>
    List the subset of classes if a user doesn’t want all the classes. Defaults to [].
    IntakeSettings DataRobotBatchPredictionJobDefinitionIntakeSettings
    A dict configuring how data is coming from.
    MaxExplanations int
    Compute prediction explanations for this amount of features.
    Name string
    The name you want your job to be identified with. Must be unique across the organization’s existing jobs.
    NumConcurrent int
    Number of concurrent chunks to score simultaneously. Defaults to the available number of cores of the deployment. Lower it to leave resources for real-time scoring.
    OutputSettings DataRobotBatchPredictionJobDefinitionOutputSettings
    A dict configuring how scored data is to be saved.
    PassthroughColumns List<string>
    Keep these columns from the scoring dataset in the scored dataset. This is useful for correlating predictions with source data.
    PassthroughColumnsSet string
    To pass through every column from the scoring dataset, set this to all.
    PredictionInstance DataRobotBatchPredictionJobDefinitionPredictionInstance
    Defaults to instance specified by deployment or system configuration.
    PredictionThreshold double
    Threshold is the point that sets the class boundary for a predicted value. This value can be set between 0.0 and 1.0.
    PredictionWarningEnabled bool
    Add prediction warnings to the scored data. Currently only supported for regression models. Defaults to False.
    Schedule DataRobotBatchPredictionJobDefinitionSchedule
    Defines at what intervals the job should run.
    SkipDriftTracking bool
    Skips drift tracking on any predictions made from this job. This is useful when running non-production workloads to not affect drift tracking and cause unnecessary alerts. Defaults to false.
    ThresholdHigh double
    Only compute prediction explanations for predictions above this threshold. Can be combined with threshold_low.
    ThresholdLow double
    Only compute prediction explanations for predictions below this threshold. Can be combined with threshold_high.
    TimeseriesSettings DataRobotBatchPredictionJobDefinitionTimeseriesSettings
    Configuration for time-series scoring.
    AbortOnError bool
    Default behavior is to abort the job if too many rows fail scoring. This will free up resources for other jobs that may score successfully. Set to false to unconditionally score every row no matter how many errors are encountered. Defaults to True.
    ChunkSize interface{}
    Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.
    ColumnNamesRemapping map[string]string
    Mapping with column renaming for output table.
    CsvSettings BatchPredictionJobDefinitionCsvSettingsArgs
    CSV intake and output settings.
    DeploymentId string
    The ID of the deployment to use for the batch prediction job.
    Enabled bool
    Whether or not the job definition should be active on a scheduled basis. If True, schedule is required.
    ExplanationAlgorithm string
    Which algorithm will be used to calculate prediction explanations.
    IncludePredictionStatus bool
    Include the prediction_status column in the output. Defaults to False.
    IncludeProbabilities bool
    Flag that enables returning of all probability columns. Defaults to True.
    IncludeProbabilitiesClasses []string
    List the subset of classes if a user doesn’t want all the classes. Defaults to [].
    IntakeSettings BatchPredictionJobDefinitionIntakeSettingsArgs
    A dict configuring how data is coming from.
    MaxExplanations int
    Compute prediction explanations for this amount of features.
    Name string
    The name you want your job to be identified with. Must be unique across the organization’s existing jobs.
    NumConcurrent int
    Number of concurrent chunks to score simultaneously. Defaults to the available number of cores of the deployment. Lower it to leave resources for real-time scoring.
    OutputSettings BatchPredictionJobDefinitionOutputSettingsArgs
    A dict configuring how scored data is to be saved.
    PassthroughColumns []string
    Keep these columns from the scoring dataset in the scored dataset. This is useful for correlating predictions with source data.
    PassthroughColumnsSet string
    To pass through every column from the scoring dataset, set this to all.
    PredictionInstance BatchPredictionJobDefinitionPredictionInstanceArgs
    Defaults to instance specified by deployment or system configuration.
    PredictionThreshold float64
    Threshold is the point that sets the class boundary for a predicted value. This value can be set between 0.0 and 1.0.
    PredictionWarningEnabled bool
    Add prediction warnings to the scored data. Currently only supported for regression models. Defaults to False.
    Schedule BatchPredictionJobDefinitionScheduleArgs
    Defines at what intervals the job should run.
    SkipDriftTracking bool
    Skips drift tracking on any predictions made from this job. This is useful when running non-production workloads to not affect drift tracking and cause unnecessary alerts. Defaults to false.
    ThresholdHigh float64
    Only compute prediction explanations for predictions above this threshold. Can be combined with threshold_low.
    ThresholdLow float64
    Only compute prediction explanations for predictions below this threshold. Can be combined with threshold_high.
    TimeseriesSettings BatchPredictionJobDefinitionTimeseriesSettingsArgs
    Configuration for time-series scoring.
    abortOnError Boolean
    Default behavior is to abort the job if too many rows fail scoring. This will free up resources for other jobs that may score successfully. Set to false to unconditionally score every row no matter how many errors are encountered. Defaults to True.
    chunkSize Object
    Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.
    columnNamesRemapping Map<String,String>
    Mapping with column renaming for output table.
    csvSettings BatchPredictionJobDefinitionCsvSettings
    CSV intake and output settings.
    deploymentId String
    The ID of the deployment to use for the batch prediction job.
    enabled Boolean
    Whether or not the job definition should be active on a scheduled basis. If True, schedule is required.
    explanationAlgorithm String
    Which algorithm will be used to calculate prediction explanations.
    includePredictionStatus Boolean
    Include the prediction_status column in the output. Defaults to False.
    includeProbabilities Boolean
    Flag that enables returning of all probability columns. Defaults to True.
    includeProbabilitiesClasses List<String>
    List the subset of classes if a user doesn’t want all the classes. Defaults to [].
    intakeSettings BatchPredictionJobDefinitionIntakeSettings
    A dict configuring how data is coming from.
    maxExplanations Integer
    Compute prediction explanations for this amount of features.
    name String
    The name you want your job to be identified with. Must be unique across the organization’s existing jobs.
    numConcurrent Integer
    Number of concurrent chunks to score simultaneously. Defaults to the available number of cores of the deployment. Lower it to leave resources for real-time scoring.
    outputSettings BatchPredictionJobDefinitionOutputSettings
    A dict configuring how scored data is to be saved.
    passthroughColumns List<String>
    Keep these columns from the scoring dataset in the scored dataset. This is useful for correlating predictions with source data.
    passthroughColumnsSet String
    To pass through every column from the scoring dataset, set this to all.
    predictionInstance BatchPredictionJobDefinitionPredictionInstance
    Defaults to instance specified by deployment or system configuration.
    predictionThreshold Double
    Threshold is the point that sets the class boundary for a predicted value. This value can be set between 0.0 and 1.0.
    predictionWarningEnabled Boolean
    Add prediction warnings to the scored data. Currently only supported for regression models. Defaults to False.
    schedule BatchPredictionJobDefinitionSchedule
    Defines at what intervals the job should run.
    skipDriftTracking Boolean
    Skips drift tracking on any predictions made from this job. This is useful when running non-production workloads to not affect drift tracking and cause unnecessary alerts. Defaults to false.
    thresholdHigh Double
    Only compute prediction explanations for predictions above this threshold. Can be combined with threshold_low.
    thresholdLow Double
    Only compute prediction explanations for predictions below this threshold. Can be combined with threshold_high.
    timeseriesSettings BatchPredictionJobDefinitionTimeseriesSettings
    Configuration for time-series scoring.
    abortOnError boolean
    Default behavior is to abort the job if too many rows fail scoring. This will free up resources for other jobs that may score successfully. Set to false to unconditionally score every row no matter how many errors are encountered. Defaults to True.
    chunkSize any
    Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.
    columnNamesRemapping {[key: string]: string}
    Mapping with column renaming for output table.
    csvSettings BatchPredictionJobDefinitionCsvSettings
    CSV intake and output settings.
    deploymentId string
    The ID of the deployment to use for the batch prediction job.
    enabled boolean
    Whether or not the job definition should be active on a scheduled basis. If True, schedule is required.
    explanationAlgorithm string
    Which algorithm will be used to calculate prediction explanations.
    includePredictionStatus boolean
    Include the prediction_status column in the output. Defaults to False.
    includeProbabilities boolean
    Flag that enables returning of all probability columns. Defaults to True.
    includeProbabilitiesClasses string[]
    List the subset of classes if a user doesn’t want all the classes. Defaults to [].
    intakeSettings BatchPredictionJobDefinitionIntakeSettings
    A dict configuring how data is coming from.
    maxExplanations number
    Compute prediction explanations for this amount of features.
    name string
    The name you want your job to be identified with. Must be unique across the organization’s existing jobs.
    numConcurrent number
    Number of concurrent chunks to score simultaneously. Defaults to the available number of cores of the deployment. Lower it to leave resources for real-time scoring.
    outputSettings BatchPredictionJobDefinitionOutputSettings
    A dict configuring how scored data is to be saved.
    passthroughColumns string[]
    Keep these columns from the scoring dataset in the scored dataset. This is useful for correlating predictions with source data.
    passthroughColumnsSet string
    To pass through every column from the scoring dataset, set this to all.
    predictionInstance BatchPredictionJobDefinitionPredictionInstance
    Defaults to instance specified by deployment or system configuration.
    predictionThreshold number
    Threshold is the point that sets the class boundary for a predicted value. This value can be set between 0.0 and 1.0.
    predictionWarningEnabled boolean
    Add prediction warnings to the scored data. Currently only supported for regression models. Defaults to False.
    schedule BatchPredictionJobDefinitionSchedule
    Defines at what intervals the job should run.
    skipDriftTracking boolean
    Skips drift tracking on any predictions made from this job. This is useful when running non-production workloads to not affect drift tracking and cause unnecessary alerts. Defaults to false.
    thresholdHigh number
    Only compute prediction explanations for predictions above this threshold. Can be combined with threshold_low.
    thresholdLow number
    Only compute prediction explanations for predictions below this threshold. Can be combined with threshold_high.
    timeseriesSettings BatchPredictionJobDefinitionTimeseriesSettings
    Configuration for time-series scoring.
    abort_on_error bool
    Default behavior is to abort the job if too many rows fail scoring. This will free up resources for other jobs that may score successfully. Set to false to unconditionally score every row no matter how many errors are encountered. Defaults to True.
    chunk_size Any
    Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.
    column_names_remapping Mapping[str, str]
    Mapping with column renaming for output table.
    csv_settings BatchPredictionJobDefinitionCsvSettingsArgs
    CSV intake and output settings.
    deployment_id str
    The ID of the deployment to use for the batch prediction job.
    enabled bool
    Whether or not the job definition should be active on a scheduled basis. If True, schedule is required.
    explanation_algorithm str
    Which algorithm will be used to calculate prediction explanations.
    include_prediction_status bool
    Include the prediction_status column in the output. Defaults to False.
    include_probabilities bool
    Flag that enables returning of all probability columns. Defaults to True.
    include_probabilities_classes Sequence[str]
    List the subset of classes if a user doesn’t want all the classes. Defaults to [].
    intake_settings BatchPredictionJobDefinitionIntakeSettingsArgs
    A dict configuring how data is coming from.
    max_explanations int
    Compute prediction explanations for this amount of features.
    name str
    The name you want your job to be identified with. Must be unique across the organization’s existing jobs.
    num_concurrent int
    Number of concurrent chunks to score simultaneously. Defaults to the available number of cores of the deployment. Lower it to leave resources for real-time scoring.
    output_settings BatchPredictionJobDefinitionOutputSettingsArgs
    A dict configuring how scored data is to be saved.
    passthrough_columns Sequence[str]
    Keep these columns from the scoring dataset in the scored dataset. This is useful for correlating predictions with source data.
    passthrough_columns_set str
    To pass through every column from the scoring dataset, set this to all.
    prediction_instance BatchPredictionJobDefinitionPredictionInstanceArgs
    Defaults to instance specified by deployment or system configuration.
    prediction_threshold float
    Threshold is the point that sets the class boundary for a predicted value. This value can be set between 0.0 and 1.0.
    prediction_warning_enabled bool
    Add prediction warnings to the scored data. Currently only supported for regression models. Defaults to False.
    schedule BatchPredictionJobDefinitionScheduleArgs
    Defines at what intervals the job should run.
    skip_drift_tracking bool
    Skips drift tracking on any predictions made from this job. This is useful when running non-production workloads to not affect drift tracking and cause unnecessary alerts. Defaults to false.
    threshold_high float
    Only compute prediction explanations for predictions above this threshold. Can be combined with threshold_low.
    threshold_low float
    Only compute prediction explanations for predictions below this threshold. Can be combined with threshold_high.
    timeseries_settings BatchPredictionJobDefinitionTimeseriesSettingsArgs
    Configuration for time-series scoring.
    abortOnError Boolean
    Default behavior is to abort the job if too many rows fail scoring. This will free up resources for other jobs that may score successfully. Set to false to unconditionally score every row no matter how many errors are encountered. Defaults to True.
    chunkSize Any
    Which strategy should be used to determine the chunk size. Can be either a named strategy or a fixed size in bytes.
    columnNamesRemapping Map<String>
    Mapping with column renaming for output table.
    csvSettings Property Map
    CSV intake and output settings.
    deploymentId String
    The ID of the deployment to use for the batch prediction job.
    enabled Boolean
    Whether or not the job definition should be active on a scheduled basis. If True, schedule is required.
    explanationAlgorithm String
    Which algorithm will be used to calculate prediction explanations.
    includePredictionStatus Boolean
    Include the prediction_status column in the output. Defaults to False.
    includeProbabilities Boolean
    Flag that enables returning of all probability columns. Defaults to True.
    includeProbabilitiesClasses List<String>
    List the subset of classes if a user doesn’t want all the classes. Defaults to [].
    intakeSettings Property Map
    A dict configuring how data is coming from.
    maxExplanations Number
    Compute prediction explanations for this amount of features.
    name String
    The name you want your job to be identified with. Must be unique across the organization’s existing jobs.
    numConcurrent Number
    Number of concurrent chunks to score simultaneously. Defaults to the available number of cores of the deployment. Lower it to leave resources for real-time scoring.
    outputSettings Property Map
    A dict configuring how scored data is to be saved.
    passthroughColumns List<String>
    Keep these columns from the scoring dataset in the scored dataset. This is useful for correlating predictions with source data.
    passthroughColumnsSet String
    To pass through every column from the scoring dataset, set this to all.
    predictionInstance Property Map
    Defaults to instance specified by deployment or system configuration.
    predictionThreshold Number
    Threshold is the point that sets the class boundary for a predicted value. This value can be set between 0.0 and 1.0.
    predictionWarningEnabled Boolean
    Add prediction warnings to the scored data. Currently only supported for regression models. Defaults to False.
    schedule Property Map
    Defines at what intervals the job should run.
    skipDriftTracking Boolean
    Skips drift tracking on any predictions made from this job. This is useful when running non-production workloads to not affect drift tracking and cause unnecessary alerts. Defaults to false.
    thresholdHigh Number
    Only compute prediction explanations for predictions above this threshold. Can be combined with threshold_low.
    thresholdLow Number
    Only compute prediction explanations for predictions below this threshold. Can be combined with threshold_high.
    timeseriesSettings Property Map
    Configuration for time-series scoring.

    Supporting Types

    BatchPredictionJobDefinitionCsvSettings, BatchPredictionJobDefinitionCsvSettingsArgs

    Delimiter string
    Fields are delimited by this character. Use the string tab to denote TSV (TAB separated values). Must be either a one-character string or the string tab.
    Encoding string
    Encoding for the CSV files.
    Quotechar string
    Fields containing the delimiter must be quoted using this character.
    Delimiter string
    Fields are delimited by this character. Use the string tab to denote TSV (TAB separated values). Must be either a one-character string or the string tab.
    Encoding string
    Encoding for the CSV files.
    Quotechar string
    Fields containing the delimiter must be quoted using this character.
    delimiter String
    Fields are delimited by this character. Use the string tab to denote TSV (TAB separated values). Must be either a one-character string or the string tab.
    encoding String
    Encoding for the CSV files.
    quotechar String
    Fields containing the delimiter must be quoted using this character.
    delimiter string
    Fields are delimited by this character. Use the string tab to denote TSV (TAB separated values). Must be either a one-character string or the string tab.
    encoding string
    Encoding for the CSV files.
    quotechar string
    Fields containing the delimiter must be quoted using this character.
    delimiter str
    Fields are delimited by this character. Use the string tab to denote TSV (TAB separated values). Must be either a one-character string or the string tab.
    encoding str
    Encoding for the CSV files.
    quotechar str
    Fields containing the delimiter must be quoted using this character.
    delimiter String
    Fields are delimited by this character. Use the string tab to denote TSV (TAB separated values). Must be either a one-character string or the string tab.
    encoding String
    Encoding for the CSV files.
    quotechar String
    Fields containing the delimiter must be quoted using this character.

    BatchPredictionJobDefinitionIntakeSettings, BatchPredictionJobDefinitionIntakeSettingsArgs

    Type string
    Type of data source.
    Catalog string
    The name of specified database catalog for JDBC type.
    CredentialId string
    The ID of the credentials for S3 or JDBC data source.
    DataStoreId string
    The ID of the external data store connected to the JDBC data source.
    DatasetId string
    The ID of the dataset to score for dataset type.
    EndpointUrl string
    Any non-default endpoint URL for S3 access.
    FetchSize int
    Changing the fetchSize can be used to balance throughput and memory usage for JDBC type.
    File string
    String path to file of scoring data for localFile type.
    Query string
    A self-supplied SELECT statement of the data set you wish to predict for JDBC type.
    Schema string
    The name of specified database schema for JDBC type.
    Table string
    The name of specified database table for JDBC type.
    Url string
    The URL to score (e.g.: s3://bucket/key) for S3 type.
    Type string
    Type of data source.
    Catalog string
    The name of specified database catalog for JDBC type.
    CredentialId string
    The ID of the credentials for S3 or JDBC data source.
    DataStoreId string
    The ID of the external data store connected to the JDBC data source.
    DatasetId string
    The ID of the dataset to score for dataset type.
    EndpointUrl string
    Any non-default endpoint URL for S3 access.
    FetchSize int
    Changing the fetchSize can be used to balance throughput and memory usage for JDBC type.
    File string
    String path to file of scoring data for localFile type.
    Query string
    A self-supplied SELECT statement of the data set you wish to predict for JDBC type.
    Schema string
    The name of specified database schema for JDBC type.
    Table string
    The name of specified database table for JDBC type.
    Url string
    The URL to score (e.g.: s3://bucket/key) for S3 type.
    type String
    Type of data source.
    catalog String
    The name of specified database catalog for JDBC type.
    credentialId String
    The ID of the credentials for S3 or JDBC data source.
    dataStoreId String
    The ID of the external data store connected to the JDBC data source.
    datasetId String
    The ID of the dataset to score for dataset type.
    endpointUrl String
    Any non-default endpoint URL for S3 access.
    fetchSize Integer
    Changing the fetchSize can be used to balance throughput and memory usage for JDBC type.
    file String
    String path to file of scoring data for localFile type.
    query String
    A self-supplied SELECT statement of the data set you wish to predict for JDBC type.
    schema String
    The name of specified database schema for JDBC type.
    table String
    The name of specified database table for JDBC type.
    url String
    The URL to score (e.g.: s3://bucket/key) for S3 type.
    type string
    Type of data source.
    catalog string
    The name of specified database catalog for JDBC type.
    credentialId string
    The ID of the credentials for S3 or JDBC data source.
    dataStoreId string
    The ID of the external data store connected to the JDBC data source.
    datasetId string
    The ID of the dataset to score for dataset type.
    endpointUrl string
    Any non-default endpoint URL for S3 access.
    fetchSize number
    Changing the fetchSize can be used to balance throughput and memory usage for JDBC type.
    file string
    String path to file of scoring data for localFile type.
    query string
    A self-supplied SELECT statement of the data set you wish to predict for JDBC type.
    schema string
    The name of specified database schema for JDBC type.
    table string
    The name of specified database table for JDBC type.
    url string
    The URL to score (e.g.: s3://bucket/key) for S3 type.
    type str
    Type of data source.
    catalog str
    The name of specified database catalog for JDBC type.
    credential_id str
    The ID of the credentials for S3 or JDBC data source.
    data_store_id str
    The ID of the external data store connected to the JDBC data source.
    dataset_id str
    The ID of the dataset to score for dataset type.
    endpoint_url str
    Any non-default endpoint URL for S3 access.
    fetch_size int
    Changing the fetchSize can be used to balance throughput and memory usage for JDBC type.
    file str
    String path to file of scoring data for localFile type.
    query str
    A self-supplied SELECT statement of the data set you wish to predict for JDBC type.
    schema str
    The name of specified database schema for JDBC type.
    table str
    The name of specified database table for JDBC type.
    url str
    The URL to score (e.g.: s3://bucket/key) for S3 type.
    type String
    Type of data source.
    catalog String
    The name of specified database catalog for JDBC type.
    credentialId String
    The ID of the credentials for S3 or JDBC data source.
    dataStoreId String
    The ID of the external data store connected to the JDBC data source.
    datasetId String
    The ID of the dataset to score for dataset type.
    endpointUrl String
    Any non-default endpoint URL for S3 access.
    fetchSize Number
    Changing the fetchSize can be used to balance throughput and memory usage for JDBC type.
    file String
    String path to file of scoring data for localFile type.
    query String
    A self-supplied SELECT statement of the data set you wish to predict for JDBC type.
    schema String
    The name of specified database schema for JDBC type.
    table String
    The name of specified database table for JDBC type.
    url String
    The URL to score (e.g.: s3://bucket/key) for S3 type.

    BatchPredictionJobDefinitionOutputSettings, BatchPredictionJobDefinitionOutputSettingsArgs

    Catalog string
    The name of specified database catalog for JDBC type.
    CreateTableIfNotExists bool
    If no existing table is detected, attempt to create it before writing data for JDBC type.
    CredentialId string
    The ID of the credentials for S3 or JDBC data source.
    DataStoreId string
    The ID of the external data store connected to the JDBC data source.
    EndpointUrl string
    Any non-default endpoint URL for S3 access.
    Path string
    Path to save the scored data as CSV for localFile type.
    Schema string
    The name of specified database schema for JDBC type.
    StatementType string
    The type of insertion statement to create for JDBC type.
    Table string
    The name of specified database table for JDBC type.
    Type string
    Type of output.
    UpdateColumns List<string>
    A list of strings containing those column names to be updated for JDBC type.
    Url string
    The URL for storing the results (e.g.: s3://bucket/key) for S3 type.
    WhereColumns List<string>
    A list of strings containing those column names to be selected for JDBC type.
    Catalog string
    The name of specified database catalog for JDBC type.
    CreateTableIfNotExists bool
    If no existing table is detected, attempt to create it before writing data for JDBC type.
    CredentialId string
    The ID of the credentials for S3 or JDBC data source.
    DataStoreId string
    The ID of the external data store connected to the JDBC data source.
    EndpointUrl string
    Any non-default endpoint URL for S3 access.
    Path string
    Path to save the scored data as CSV for localFile type.
    Schema string
    The name of specified database schema for JDBC type.
    StatementType string
    The type of insertion statement to create for JDBC type.
    Table string
    The name of specified database table for JDBC type.
    Type string
    Type of output.
    UpdateColumns []string
    A list of strings containing those column names to be updated for JDBC type.
    Url string
    The URL for storing the results (e.g.: s3://bucket/key) for S3 type.
    WhereColumns []string
    A list of strings containing those column names to be selected for JDBC type.
    catalog String
    The name of specified database catalog for JDBC type.
    createTableIfNotExists Boolean
    If no existing table is detected, attempt to create it before writing data for JDBC type.
    credentialId String
    The ID of the credentials for S3 or JDBC data source.
    dataStoreId String
    The ID of the external data store connected to the JDBC data source.
    endpointUrl String
    Any non-default endpoint URL for S3 access.
    path String
    Path to save the scored data as CSV for localFile type.
    schema String
    The name of specified database schema for JDBC type.
    statementType String
    The type of insertion statement to create for JDBC type.
    table String
    The name of specified database table for JDBC type.
    type String
    Type of output.
    updateColumns List<String>
    A list of strings containing those column names to be updated for JDBC type.
    url String
    The URL for storing the results (e.g.: s3://bucket/key) for S3 type.
    whereColumns List<String>
    A list of strings containing those column names to be selected for JDBC type.
    catalog string
    The name of specified database catalog for JDBC type.
    createTableIfNotExists boolean
    If no existing table is detected, attempt to create it before writing data for JDBC type.
    credentialId string
    The ID of the credentials for S3 or JDBC data source.
    dataStoreId string
    The ID of the external data store connected to the JDBC data source.
    endpointUrl string
    Any non-default endpoint URL for S3 access.
    path string
    Path to save the scored data as CSV for localFile type.
    schema string
    The name of specified database schema for JDBC type.
    statementType string
    The type of insertion statement to create for JDBC type.
    table string
    The name of specified database table for JDBC type.
    type string
    Type of output.
    updateColumns string[]
    A list of strings containing those column names to be updated for JDBC type.
    url string
    The URL for storing the results (e.g.: s3://bucket/key) for S3 type.
    whereColumns string[]
    A list of strings containing those column names to be selected for JDBC type.
    catalog str
    The name of specified database catalog for JDBC type.
    create_table_if_not_exists bool
    If no existing table is detected, attempt to create it before writing data for JDBC type.
    credential_id str
    The ID of the credentials for S3 or JDBC data source.
    data_store_id str
    The ID of the external data store connected to the JDBC data source.
    endpoint_url str
    Any non-default endpoint URL for S3 access.
    path str
    Path to save the scored data as CSV for localFile type.
    schema str
    The name of specified database schema for JDBC type.
    statement_type str
    The type of insertion statement to create for JDBC type.
    table str
    The name of specified database table for JDBC type.
    type str
    Type of output.
    update_columns Sequence[str]
    A list of strings containing those column names to be updated for JDBC type.
    url str
    The URL for storing the results (e.g.: s3://bucket/key) for S3 type.
    where_columns Sequence[str]
    A list of strings containing those column names to be selected for JDBC type.
    catalog String
    The name of specified database catalog for JDBC type.
    createTableIfNotExists Boolean
    If no existing table is detected, attempt to create it before writing data for JDBC type.
    credentialId String
    The ID of the credentials for S3 or JDBC data source.
    dataStoreId String
    The ID of the external data store connected to the JDBC data source.
    endpointUrl String
    Any non-default endpoint URL for S3 access.
    path String
    Path to save the scored data as CSV for localFile type.
    schema String
    The name of specified database schema for JDBC type.
    statementType String
    The type of insertion statement to create for JDBC type.
    table String
    The name of specified database table for JDBC type.
    type String
    Type of output.
    updateColumns List<String>
    A list of strings containing those column names to be updated for JDBC type.
    url String
    The URL for storing the results (e.g.: s3://bucket/key) for S3 type.
    whereColumns List<String>
    A list of strings containing those column names to be selected for JDBC type.

    BatchPredictionJobDefinitionPredictionInstance, BatchPredictionJobDefinitionPredictionInstanceArgs

    HostName string
    Hostname of the prediction instance.
    ApiKey string
    By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.
    DatarobotKey string
    If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.
    SslEnabled bool
    Set to false to run prediction requests from the batch prediction job without SSL. Defaults to true.
    HostName string
    Hostname of the prediction instance.
    ApiKey string
    By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.
    DatarobotKey string
    If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.
    SslEnabled bool
    Set to false to run prediction requests from the batch prediction job without SSL. Defaults to true.
    hostName String
    Hostname of the prediction instance.
    apiKey String
    By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.
    datarobotKey String
    If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.
    sslEnabled Boolean
    Set to false to run prediction requests from the batch prediction job without SSL. Defaults to true.
    hostName string
    Hostname of the prediction instance.
    apiKey string
    By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.
    datarobotKey string
    If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.
    sslEnabled boolean
    Set to false to run prediction requests from the batch prediction job without SSL. Defaults to true.
    host_name str
    Hostname of the prediction instance.
    api_key str
    By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.
    datarobot_key str
    If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.
    ssl_enabled bool
    Set to false to run prediction requests from the batch prediction job without SSL. Defaults to true.
    hostName String
    Hostname of the prediction instance.
    apiKey String
    By default, prediction requests will use the API key of the user that created the job. This allows you to make requests on behalf of other users.
    datarobotKey String
    If running a job against a prediction instance in the Managed AI Cloud, you must provide the organization level DataRobot-Key.
    sslEnabled Boolean
    Set to false to run prediction requests from the batch prediction job without SSL. Defaults to true.

    BatchPredictionJobDefinitionSchedule, BatchPredictionJobDefinitionScheduleArgs

    DayOfMonths List<string>
    Days of the month when the job will run.
    DayOfWeeks List<string>
    Days of the week when the job will run.
    Hours List<string>
    Hours of the day when the job will run.
    Minutes List<string>
    Minutes of the day when the job will run.
    Months List<string>
    Months of the year when the job will run.
    DayOfMonths []string
    Days of the month when the job will run.
    DayOfWeeks []string
    Days of the week when the job will run.
    Hours []string
    Hours of the day when the job will run.
    Minutes []string
    Minutes of the day when the job will run.
    Months []string
    Months of the year when the job will run.
    dayOfMonths List<String>
    Days of the month when the job will run.
    dayOfWeeks List<String>
    Days of the week when the job will run.
    hours List<String>
    Hours of the day when the job will run.
    minutes List<String>
    Minutes of the day when the job will run.
    months List<String>
    Months of the year when the job will run.
    dayOfMonths string[]
    Days of the month when the job will run.
    dayOfWeeks string[]
    Days of the week when the job will run.
    hours string[]
    Hours of the day when the job will run.
    minutes string[]
    Minutes of the day when the job will run.
    months string[]
    Months of the year when the job will run.
    day_of_months Sequence[str]
    Days of the month when the job will run.
    day_of_weeks Sequence[str]
    Days of the week when the job will run.
    hours Sequence[str]
    Hours of the day when the job will run.
    minutes Sequence[str]
    Minutes of the day when the job will run.
    months Sequence[str]
    Months of the year when the job will run.
    dayOfMonths List<String>
    Days of the month when the job will run.
    dayOfWeeks List<String>
    Days of the week when the job will run.
    hours List<String>
    Hours of the day when the job will run.
    minutes List<String>
    Minutes of the day when the job will run.
    months List<String>
    Months of the year when the job will run.

    BatchPredictionJobDefinitionTimeseriesSettings, BatchPredictionJobDefinitionTimeseriesSettingsArgs

    ForecastPoint string
    Forecast point for the dataset, used for the forecast predictions. May be passed if timeseries_settings.type=forecast.
    PredictionsEndDate string
    End date for historical predictions. May be passed if timeseries_settings.type=historical.
    PredictionsStartDate string
    Start date for historical predictions. May be passed if timeseries_settings.type=historical.
    RelaxKnownInAdvanceFeaturesCheck bool
    If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. Default is False.
    Type string
    Type of time-series prediction. Must be 'forecast' or 'historical'. Default is 'forecast'.
    ForecastPoint string
    Forecast point for the dataset, used for the forecast predictions. May be passed if timeseries_settings.type=forecast.
    PredictionsEndDate string
    End date for historical predictions. May be passed if timeseries_settings.type=historical.
    PredictionsStartDate string
    Start date for historical predictions. May be passed if timeseries_settings.type=historical.
    RelaxKnownInAdvanceFeaturesCheck bool
    If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. Default is False.
    Type string
    Type of time-series prediction. Must be 'forecast' or 'historical'. Default is 'forecast'.
    forecastPoint String
    Forecast point for the dataset, used for the forecast predictions. May be passed if timeseries_settings.type=forecast.
    predictionsEndDate String
    End date for historical predictions. May be passed if timeseries_settings.type=historical.
    predictionsStartDate String
    Start date for historical predictions. May be passed if timeseries_settings.type=historical.
    relaxKnownInAdvanceFeaturesCheck Boolean
    If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. Default is False.
    type String
    Type of time-series prediction. Must be 'forecast' or 'historical'. Default is 'forecast'.
    forecastPoint string
    Forecast point for the dataset, used for the forecast predictions. May be passed if timeseries_settings.type=forecast.
    predictionsEndDate string
    End date for historical predictions. May be passed if timeseries_settings.type=historical.
    predictionsStartDate string
    Start date for historical predictions. May be passed if timeseries_settings.type=historical.
    relaxKnownInAdvanceFeaturesCheck boolean
    If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. Default is False.
    type string
    Type of time-series prediction. Must be 'forecast' or 'historical'. Default is 'forecast'.
    forecast_point str
    Forecast point for the dataset, used for the forecast predictions. May be passed if timeseries_settings.type=forecast.
    predictions_end_date str
    End date for historical predictions. May be passed if timeseries_settings.type=historical.
    predictions_start_date str
    Start date for historical predictions. May be passed if timeseries_settings.type=historical.
    relax_known_in_advance_features_check bool
    If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. Default is False.
    type str
    Type of time-series prediction. Must be 'forecast' or 'historical'. Default is 'forecast'.
    forecastPoint String
    Forecast point for the dataset, used for the forecast predictions. May be passed if timeseries_settings.type=forecast.
    predictionsEndDate String
    End date for historical predictions. May be passed if timeseries_settings.type=historical.
    predictionsStartDate String
    Start date for historical predictions. May be passed if timeseries_settings.type=historical.
    relaxKnownInAdvanceFeaturesCheck Boolean
    If True, missing values in the known in advance features are allowed in the forecast window at the prediction time. Default is False.
    type String
    Type of time-series prediction. Must be 'forecast' or 'historical'. Default is 'forecast'.

    Package Details

    Repository
    datarobot datarobot-community/pulumi-datarobot
    License
    Apache-2.0
    Notes
    This Pulumi package is based on the datarobot Terraform Provider.
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    DataRobot v0.8.16 published on Tuesday, Mar 18, 2025 by DataRobot, Inc.
    datarobot-community/pulumi-datarobot