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Oracle Cloud Infrastructure v2.33.0 published on Thursday, May 1, 2025 by Pulumi
pulumi/pulumi-oci

oci.AiVision.Model

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Oracle Cloud Infrastructure v2.33.0 published on Thursday, May 1, 2025 by Pulumi
pulumi/pulumi-oci

    This resource provides the Model resource in Oracle Cloud Infrastructure Ai Vision service.

    Creates a new Model.

    Example Usage

    import * as pulumi from "@pulumi/pulumi";
import * as oci from "@pulumi/oci";

const testModel = new oci.aivision.Model("test_model", {
    compartmentId: compartmentId,
    modelType: modelModelType,
    projectId: testProject.id,
    trainingDataset: {
        datasetType: modelTrainingDatasetDatasetType,
        bucket: modelTrainingDatasetBucket,
        datasetId: testDataset.id,
        namespaceName: modelTrainingDatasetNamespace,
        object: modelTrainingDatasetObject,
    },
    definedTags: {
        "foo-namespace.bar-key": "value",
    },
    description: modelDescription,
    displayName: modelDisplayName,
    freeformTags: {
        "bar-key": "value",
    },
    isQuickMode: modelIsQuickMode,
    maxTrainingDurationInHours: modelMaxTrainingDurationInHours,
    modelVersion: modelModelVersion,
    testingDataset: {
        datasetType: modelTestingDatasetDatasetType,
        bucket: modelTestingDatasetBucket,
        datasetId: testDataset.id,
        namespaceName: modelTestingDatasetNamespace,
        object: modelTestingDatasetObject,
    },
    validationDataset: {
        datasetType: modelValidationDatasetDatasetType,
        bucket: modelValidationDatasetBucket,
        datasetId: testDataset.id,
        namespaceName: modelValidationDatasetNamespace,
        object: modelValidationDatasetObject,
    },
});

    import pulumi
import pulumi_oci as oci

test_model = oci.ai_vision.Model("test_model",
    compartment_id=compartment_id,
    model_type=model_model_type,
    project_id=test_project["id"],
    training_dataset={
        "dataset_type": model_training_dataset_dataset_type,
        "bucket": model_training_dataset_bucket,
        "dataset_id": test_dataset["id"],
        "namespace_name": model_training_dataset_namespace,
        "object": model_training_dataset_object,
    },
    defined_tags={
        "foo-namespace.bar-key": "value",
    },
    description=model_description,
    display_name=model_display_name,
    freeform_tags={
        "bar-key": "value",
    },
    is_quick_mode=model_is_quick_mode,
    max_training_duration_in_hours=model_max_training_duration_in_hours,
    model_version=model_model_version,
    testing_dataset={
        "dataset_type": model_testing_dataset_dataset_type,
        "bucket": model_testing_dataset_bucket,
        "dataset_id": test_dataset["id"],
        "namespace_name": model_testing_dataset_namespace,
        "object": model_testing_dataset_object,
    },
    validation_dataset={
        "dataset_type": model_validation_dataset_dataset_type,
        "bucket": model_validation_dataset_bucket,
        "dataset_id": test_dataset["id"],
        "namespace_name": model_validation_dataset_namespace,
        "object": model_validation_dataset_object,
    })

    package main

import (
	"github.com/pulumi/pulumi-oci/sdk/v2/go/oci/aivision"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := aivision.NewModel(ctx, "test_model", &aivision.ModelArgs{
			CompartmentId: pulumi.Any(compartmentId),
			ModelType:     pulumi.Any(modelModelType),
			ProjectId:     pulumi.Any(testProject.Id),
			TrainingDataset: &aivision.ModelTrainingDatasetArgs{
				DatasetType:   pulumi.Any(modelTrainingDatasetDatasetType),
				Bucket:        pulumi.Any(modelTrainingDatasetBucket),
				DatasetId:     pulumi.Any(testDataset.Id),
				NamespaceName: pulumi.Any(modelTrainingDatasetNamespace),
				Object:        pulumi.Any(modelTrainingDatasetObject),
			},
			DefinedTags: pulumi.StringMap{
				"foo-namespace.bar-key": pulumi.String("value"),
			},
			Description: pulumi.Any(modelDescription),
			DisplayName: pulumi.Any(modelDisplayName),
			FreeformTags: pulumi.StringMap{
				"bar-key": pulumi.String("value"),
			},
			IsQuickMode:                pulumi.Any(modelIsQuickMode),
			MaxTrainingDurationInHours: pulumi.Any(modelMaxTrainingDurationInHours),
			ModelVersion:               pulumi.Any(modelModelVersion),
			TestingDataset: &aivision.ModelTestingDatasetArgs{
				DatasetType:   pulumi.Any(modelTestingDatasetDatasetType),
				Bucket:        pulumi.Any(modelTestingDatasetBucket),
				DatasetId:     pulumi.Any(testDataset.Id),
				NamespaceName: pulumi.Any(modelTestingDatasetNamespace),
				Object:        pulumi.Any(modelTestingDatasetObject),
			},
			ValidationDataset: &aivision.ModelValidationDatasetArgs{
				DatasetType:   pulumi.Any(modelValidationDatasetDatasetType),
				Bucket:        pulumi.Any(modelValidationDatasetBucket),
				DatasetId:     pulumi.Any(testDataset.Id),
				NamespaceName: pulumi.Any(modelValidationDatasetNamespace),
				Object:        pulumi.Any(modelValidationDatasetObject),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

    using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Oci = Pulumi.Oci;

return await Deployment.RunAsync(() => 
{
    var testModel = new Oci.AiVision.Model("test_model", new()
    {
        CompartmentId = compartmentId,
        ModelType = modelModelType,
        ProjectId = testProject.Id,
        TrainingDataset = new Oci.AiVision.Inputs.ModelTrainingDatasetArgs
        {
            DatasetType = modelTrainingDatasetDatasetType,
            Bucket = modelTrainingDatasetBucket,
            DatasetId = testDataset.Id,
            NamespaceName = modelTrainingDatasetNamespace,
            Object = modelTrainingDatasetObject,
        },
        DefinedTags = 
        {
            { "foo-namespace.bar-key", "value" },
        },
        Description = modelDescription,
        DisplayName = modelDisplayName,
        FreeformTags = 
        {
            { "bar-key", "value" },
        },
        IsQuickMode = modelIsQuickMode,
        MaxTrainingDurationInHours = modelMaxTrainingDurationInHours,
        ModelVersion = modelModelVersion,
        TestingDataset = new Oci.AiVision.Inputs.ModelTestingDatasetArgs
        {
            DatasetType = modelTestingDatasetDatasetType,
            Bucket = modelTestingDatasetBucket,
            DatasetId = testDataset.Id,
            NamespaceName = modelTestingDatasetNamespace,
            Object = modelTestingDatasetObject,
        },
        ValidationDataset = new Oci.AiVision.Inputs.ModelValidationDatasetArgs
        {
            DatasetType = modelValidationDatasetDatasetType,
            Bucket = modelValidationDatasetBucket,
            DatasetId = testDataset.Id,
            NamespaceName = modelValidationDatasetNamespace,
            Object = modelValidationDatasetObject,
        },
    });

});

    package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.oci.AiVision.Model;
import com.pulumi.oci.AiVision.ModelArgs;
import com.pulumi.oci.AiVision.inputs.ModelTrainingDatasetArgs;
import com.pulumi.oci.AiVision.inputs.ModelTestingDatasetArgs;
import com.pulumi.oci.AiVision.inputs.ModelValidationDatasetArgs;
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 testModel = new Model("testModel", ModelArgs.builder()
            .compartmentId(compartmentId)
            .modelType(modelModelType)
            .projectId(testProject.id())
            .trainingDataset(ModelTrainingDatasetArgs.builder()
                .datasetType(modelTrainingDatasetDatasetType)
                .bucket(modelTrainingDatasetBucket)
                .datasetId(testDataset.id())
                .namespaceName(modelTrainingDatasetNamespace)
                .object(modelTrainingDatasetObject)
                .build())
            .definedTags(Map.of("foo-namespace.bar-key", "value"))
            .description(modelDescription)
            .displayName(modelDisplayName)
            .freeformTags(Map.of("bar-key", "value"))
            .isQuickMode(modelIsQuickMode)
            .maxTrainingDurationInHours(modelMaxTrainingDurationInHours)
            .modelVersion(modelModelVersion)
            .testingDataset(ModelTestingDatasetArgs.builder()
                .datasetType(modelTestingDatasetDatasetType)
                .bucket(modelTestingDatasetBucket)
                .datasetId(testDataset.id())
                .namespaceName(modelTestingDatasetNamespace)
                .object(modelTestingDatasetObject)
                .build())
            .validationDataset(ModelValidationDatasetArgs.builder()
                .datasetType(modelValidationDatasetDatasetType)
                .bucket(modelValidationDatasetBucket)
                .datasetId(testDataset.id())
                .namespaceName(modelValidationDatasetNamespace)
                .object(modelValidationDatasetObject)
                .build())
            .build());

    }
}

    resources:
  testModel:
    type: oci:AiVision:Model
    name: test_model
    properties:
      compartmentId: ${compartmentId}
      modelType: ${modelModelType}
      projectId: ${testProject.id}
      trainingDataset:
        datasetType: ${modelTrainingDatasetDatasetType}
        bucket: ${modelTrainingDatasetBucket}
        datasetId: ${testDataset.id}
        namespaceName: ${modelTrainingDatasetNamespace}
        object: ${modelTrainingDatasetObject}
      definedTags:
        foo-namespace.bar-key: value
      description: ${modelDescription}
      displayName: ${modelDisplayName}
      freeformTags:
        bar-key: value
      isQuickMode: ${modelIsQuickMode}
      maxTrainingDurationInHours: ${modelMaxTrainingDurationInHours}
      modelVersion: ${modelModelVersion}
      testingDataset:
        datasetType: ${modelTestingDatasetDatasetType}
        bucket: ${modelTestingDatasetBucket}
        datasetId: ${testDataset.id}
        namespaceName: ${modelTestingDatasetNamespace}
        object: ${modelTestingDatasetObject}
      validationDataset:
        datasetType: ${modelValidationDatasetDatasetType}
        bucket: ${modelValidationDatasetBucket}
        datasetId: ${testDataset.id}
        namespaceName: ${modelValidationDatasetNamespace}
        object: ${modelValidationDatasetObject}

    Create Model Resource

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

    Constructor syntax

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

@overload
def Model(resource_name: str,
          opts: Optional[ResourceOptions] = None,
          model_type: Optional[str] = None,
          training_dataset: Optional[ModelTrainingDatasetArgs] = None,
          project_id: Optional[str] = None,
          compartment_id: Optional[str] = None,
          display_name: Optional[str] = None,
          is_quick_mode: Optional[bool] = None,
          max_training_duration_in_hours: Optional[float] = None,
          freeform_tags: Optional[Mapping[str, str]] = None,
          model_version: Optional[str] = None,
          description: Optional[str] = None,
          testing_dataset: Optional[ModelTestingDatasetArgs] = None,
          defined_tags: Optional[Mapping[str, str]] = None,
          validation_dataset: Optional[ModelValidationDatasetArgs] = None)
    func NewModel(ctx *Context, name string, args ModelArgs, opts ...ResourceOption) (*Model, error)
    public Model(string name, ModelArgs args, CustomResourceOptions? opts = null)
    public Model(String name, ModelArgs args)
public Model(String name, ModelArgs args, CustomResourceOptions options)

    type: oci:AiVision:Model
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 ModelArgs
    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 ModelArgs
    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 ModelArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args ModelArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args ModelArgs
    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 examplemodelResourceResourceFromAiVisionmodel = new Oci.AiVision.Model("examplemodelResourceResourceFromAiVisionmodel", new()
{
    ModelType = "string",
    TrainingDataset = new Oci.AiVision.Inputs.ModelTrainingDatasetArgs
    {
        DatasetType = "string",
        Bucket = "string",
        DatasetId = "string",
        NamespaceName = "string",
        Object = "string",
    },
    ProjectId = "string",
    CompartmentId = "string",
    DisplayName = "string",
    IsQuickMode = false,
    MaxTrainingDurationInHours = 0,
    FreeformTags = 
    {
        { "string", "string" },
    },
    ModelVersion = "string",
    Description = "string",
    TestingDataset = new Oci.AiVision.Inputs.ModelTestingDatasetArgs
    {
        DatasetType = "string",
        Bucket = "string",
        DatasetId = "string",
        NamespaceName = "string",
        Object = "string",
    },
    DefinedTags = 
    {
        { "string", "string" },
    },
    ValidationDataset = new Oci.AiVision.Inputs.ModelValidationDatasetArgs
    {
        DatasetType = "string",
        Bucket = "string",
        DatasetId = "string",
        NamespaceName = "string",
        Object = "string",
    },
});

    example, err := aivision.NewModel(ctx, "examplemodelResourceResourceFromAiVisionmodel", &aivision.ModelArgs{
	ModelType: pulumi.String("string"),
	TrainingDataset: &aivision.ModelTrainingDatasetArgs{
		DatasetType:   pulumi.String("string"),
		Bucket:        pulumi.String("string"),
		DatasetId:     pulumi.String("string"),
		NamespaceName: pulumi.String("string"),
		Object:        pulumi.String("string"),
	},
	ProjectId:                  pulumi.String("string"),
	CompartmentId:              pulumi.String("string"),
	DisplayName:                pulumi.String("string"),
	IsQuickMode:                pulumi.Bool(false),
	MaxTrainingDurationInHours: pulumi.Float64(0),
	FreeformTags: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	ModelVersion: pulumi.String("string"),
	Description:  pulumi.String("string"),
	TestingDataset: &aivision.ModelTestingDatasetArgs{
		DatasetType:   pulumi.String("string"),
		Bucket:        pulumi.String("string"),
		DatasetId:     pulumi.String("string"),
		NamespaceName: pulumi.String("string"),
		Object:        pulumi.String("string"),
	},
	DefinedTags: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	ValidationDataset: &aivision.ModelValidationDatasetArgs{
		DatasetType:   pulumi.String("string"),
		Bucket:        pulumi.String("string"),
		DatasetId:     pulumi.String("string"),
		NamespaceName: pulumi.String("string"),
		Object:        pulumi.String("string"),
	},
})

    var examplemodelResourceResourceFromAiVisionmodel = new com.pulumi.oci.AiVision.Model("examplemodelResourceResourceFromAiVisionmodel", com.pulumi.oci.AiVision.ModelArgs.builder()
    .modelType("string")
    .trainingDataset(ModelTrainingDatasetArgs.builder()
        .datasetType("string")
        .bucket("string")
        .datasetId("string")
        .namespaceName("string")
        .object("string")
        .build())
    .projectId("string")
    .compartmentId("string")
    .displayName("string")
    .isQuickMode(false)
    .maxTrainingDurationInHours(0.0)
    .freeformTags(Map.of("string", "string"))
    .modelVersion("string")
    .description("string")
    .testingDataset(ModelTestingDatasetArgs.builder()
        .datasetType("string")
        .bucket("string")
        .datasetId("string")
        .namespaceName("string")
        .object("string")
        .build())
    .definedTags(Map.of("string", "string"))
    .validationDataset(ModelValidationDatasetArgs.builder()
        .datasetType("string")
        .bucket("string")
        .datasetId("string")
        .namespaceName("string")
        .object("string")
        .build())
    .build());

    examplemodel_resource_resource_from_ai_visionmodel = oci.ai_vision.Model("examplemodelResourceResourceFromAiVisionmodel",
    model_type="string",
    training_dataset={
        "dataset_type": "string",
        "bucket": "string",
        "dataset_id": "string",
        "namespace_name": "string",
        "object": "string",
    },
    project_id="string",
    compartment_id="string",
    display_name="string",
    is_quick_mode=False,
    max_training_duration_in_hours=0,
    freeform_tags={
        "string": "string",
    },
    model_version="string",
    description="string",
    testing_dataset={
        "dataset_type": "string",
        "bucket": "string",
        "dataset_id": "string",
        "namespace_name": "string",
        "object": "string",
    },
    defined_tags={
        "string": "string",
    },
    validation_dataset={
        "dataset_type": "string",
        "bucket": "string",
        "dataset_id": "string",
        "namespace_name": "string",
        "object": "string",
    })

    const examplemodelResourceResourceFromAiVisionmodel = new oci.aivision.Model("examplemodelResourceResourceFromAiVisionmodel", {
    modelType: "string",
    trainingDataset: {
        datasetType: "string",
        bucket: "string",
        datasetId: "string",
        namespaceName: "string",
        object: "string",
    },
    projectId: "string",
    compartmentId: "string",
    displayName: "string",
    isQuickMode: false,
    maxTrainingDurationInHours: 0,
    freeformTags: {
        string: "string",
    },
    modelVersion: "string",
    description: "string",
    testingDataset: {
        datasetType: "string",
        bucket: "string",
        datasetId: "string",
        namespaceName: "string",
        object: "string",
    },
    definedTags: {
        string: "string",
    },
    validationDataset: {
        datasetType: "string",
        bucket: "string",
        datasetId: "string",
        namespaceName: "string",
        object: "string",
    },
});

    type: oci:AiVision:Model
properties:
    compartmentId: string
    definedTags:
        string: string
    description: string
    displayName: string
    freeformTags:
        string: string
    isQuickMode: false
    maxTrainingDurationInHours: 0
    modelType: string
    modelVersion: string
    projectId: string
    testingDataset:
        bucket: string
        datasetId: string
        datasetType: string
        namespaceName: string
        object: string
    trainingDataset:
        bucket: string
        datasetId: string
        datasetType: string
        namespaceName: string
        object: string
    validationDataset:
        bucket: string
        datasetId: string
        datasetType: string
        namespaceName: string
        object: string

    Model 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 Model resource accepts the following input properties:

    CompartmentId string
    (Updatable) Compartment Identifier
    ModelType string
    The type of the model.
    ProjectId string
    The OCID of the project to associate with the model.
    TrainingDataset ModelTrainingDataset
    The base entity for a Dataset, which is the input for Model creation.
    DefinedTags Dictionary<string, string>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    Description string
    (Updatable) A short description of the Model.
    DisplayName string
    (Updatable) Model Identifier
    FreeformTags Dictionary<string, string>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    IsQuickMode bool
    If It's true, Training is set for recommended epochs needed for quick training.
    MaxTrainingDurationInHours double
    The maximum duration in hours for which the training will run.
    ModelVersion string
    Model version.
    TestingDataset ModelTestingDataset
    The base entity for a Dataset, which is the input for Model creation.
    ValidationDataset ModelValidationDataset
    The base entity for a Dataset, which is the input for Model creation.
    CompartmentId string
    (Updatable) Compartment Identifier
    ModelType string
    The type of the model.
    ProjectId string
    The OCID of the project to associate with the model.
    TrainingDataset ModelTrainingDatasetArgs
    The base entity for a Dataset, which is the input for Model creation.
    DefinedTags map[string]string
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    Description string
    (Updatable) A short description of the Model.
    DisplayName string
    (Updatable) Model Identifier
    FreeformTags map[string]string
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    IsQuickMode bool
    If It's true, Training is set for recommended epochs needed for quick training.
    MaxTrainingDurationInHours float64
    The maximum duration in hours for which the training will run.
    ModelVersion string
    Model version.
    TestingDataset ModelTestingDatasetArgs
    The base entity for a Dataset, which is the input for Model creation.
    ValidationDataset ModelValidationDatasetArgs
    The base entity for a Dataset, which is the input for Model creation.
    compartmentId String
    (Updatable) Compartment Identifier
    modelType String
    The type of the model.
    projectId String
    The OCID of the project to associate with the model.
    trainingDataset ModelTrainingDataset
    The base entity for a Dataset, which is the input for Model creation.
    definedTags Map<String,String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description String
    (Updatable) A short description of the Model.
    displayName String
    (Updatable) Model Identifier
    freeformTags Map<String,String>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    isQuickMode Boolean
    If It's true, Training is set for recommended epochs needed for quick training.
    maxTrainingDurationInHours Double
    The maximum duration in hours for which the training will run.
    modelVersion String
    Model version.
    testingDataset ModelTestingDataset
    The base entity for a Dataset, which is the input for Model creation.
    validationDataset ModelValidationDataset
    The base entity for a Dataset, which is the input for Model creation.
    compartmentId string
    (Updatable) Compartment Identifier
    modelType string
    The type of the model.
    projectId string
    The OCID of the project to associate with the model.
    trainingDataset ModelTrainingDataset
    The base entity for a Dataset, which is the input for Model creation.
    definedTags {[key: string]: string}
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description string
    (Updatable) A short description of the Model.
    displayName string
    (Updatable) Model Identifier
    freeformTags {[key: string]: string}
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    isQuickMode boolean
    If It's true, Training is set for recommended epochs needed for quick training.
    maxTrainingDurationInHours number
    The maximum duration in hours for which the training will run.
    modelVersion string
    Model version.
    testingDataset ModelTestingDataset
    The base entity for a Dataset, which is the input for Model creation.
    validationDataset ModelValidationDataset
    The base entity for a Dataset, which is the input for Model creation.
    compartment_id str
    (Updatable) Compartment Identifier
    model_type str
    The type of the model.
    project_id str
    The OCID of the project to associate with the model.
    training_dataset ModelTrainingDatasetArgs
    The base entity for a Dataset, which is the input for Model creation.
    defined_tags Mapping[str, str]
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description str
    (Updatable) A short description of the Model.
    display_name str
    (Updatable) Model Identifier
    freeform_tags Mapping[str, str]
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    is_quick_mode bool
    If It's true, Training is set for recommended epochs needed for quick training.
    max_training_duration_in_hours float
    The maximum duration in hours for which the training will run.
    model_version str
    Model version.
    testing_dataset ModelTestingDatasetArgs
    The base entity for a Dataset, which is the input for Model creation.
    validation_dataset ModelValidationDatasetArgs
    The base entity for a Dataset, which is the input for Model creation.
    compartmentId String
    (Updatable) Compartment Identifier
    modelType String
    The type of the model.
    projectId String
    The OCID of the project to associate with the model.
    trainingDataset Property Map
    The base entity for a Dataset, which is the input for Model creation.
    definedTags Map<String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description String
    (Updatable) A short description of the Model.
    displayName String
    (Updatable) Model Identifier
    freeformTags Map<String>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    isQuickMode Boolean
    If It's true, Training is set for recommended epochs needed for quick training.
    maxTrainingDurationInHours Number
    The maximum duration in hours for which the training will run.
    modelVersion String
    Model version.
    testingDataset Property Map
    The base entity for a Dataset, which is the input for Model creation.
    validationDataset Property Map
    The base entity for a Dataset, which is the input for Model creation.

    Outputs

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

    AveragePrecision double
    Average precision of the trained model
    ConfidenceThreshold double
    Confidence ratio of the calculation
    Id string
    The provider-assigned unique ID for this managed resource.
    LifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    Metrics string
    Complete Training Metrics for successful trained model
    Precision double
    Precision of the trained model
    Recall double
    Recall of the trained model
    State string
    The current state of the Model.
    SystemTags Dictionary<string, string>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    TestImageCount int
    Total number of testing Images
    TimeCreated string
    The time the Model was created. An RFC3339 formatted datetime string
    TimeUpdated string
    The time the Model was updated. An RFC3339 formatted datetime string
    TotalImageCount int
    Total number of training Images
    TrainedDurationInHours double
    Total hours actually used for training
    AveragePrecision float64
    Average precision of the trained model
    ConfidenceThreshold float64
    Confidence ratio of the calculation
    Id string
    The provider-assigned unique ID for this managed resource.
    LifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    Metrics string
    Complete Training Metrics for successful trained model
    Precision float64
    Precision of the trained model
    Recall float64
    Recall of the trained model
    State string
    The current state of the Model.
    SystemTags map[string]string
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    TestImageCount int
    Total number of testing Images
    TimeCreated string
    The time the Model was created. An RFC3339 formatted datetime string
    TimeUpdated string
    The time the Model was updated. An RFC3339 formatted datetime string
    TotalImageCount int
    Total number of training Images
    TrainedDurationInHours float64
    Total hours actually used for training
    averagePrecision Double
    Average precision of the trained model
    confidenceThreshold Double
    Confidence ratio of the calculation
    id String
    The provider-assigned unique ID for this managed resource.
    lifecycleDetails String
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    metrics String
    Complete Training Metrics for successful trained model
    precision Double
    Precision of the trained model
    recall Double
    Recall of the trained model
    state String
    The current state of the Model.
    systemTags Map<String,String>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    testImageCount Integer
    Total number of testing Images
    timeCreated String
    The time the Model was created. An RFC3339 formatted datetime string
    timeUpdated String
    The time the Model was updated. An RFC3339 formatted datetime string
    totalImageCount Integer
    Total number of training Images
    trainedDurationInHours Double
    Total hours actually used for training
    averagePrecision number
    Average precision of the trained model
    confidenceThreshold number
    Confidence ratio of the calculation
    id string
    The provider-assigned unique ID for this managed resource.
    lifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    metrics string
    Complete Training Metrics for successful trained model
    precision number
    Precision of the trained model
    recall number
    Recall of the trained model
    state string
    The current state of the Model.
    systemTags {[key: string]: string}
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    testImageCount number
    Total number of testing Images
    timeCreated string
    The time the Model was created. An RFC3339 formatted datetime string
    timeUpdated string
    The time the Model was updated. An RFC3339 formatted datetime string
    totalImageCount number
    Total number of training Images
    trainedDurationInHours number
    Total hours actually used for training
    average_precision float
    Average precision of the trained model
    confidence_threshold float
    Confidence ratio of the calculation
    id str
    The provider-assigned unique ID for this managed resource.
    lifecycle_details str
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    metrics str
    Complete Training Metrics for successful trained model
    precision float
    Precision of the trained model
    recall float
    Recall of the trained model
    state str
    The current state of the Model.
    system_tags Mapping[str, str]
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    test_image_count int
    Total number of testing Images
    time_created str
    The time the Model was created. An RFC3339 formatted datetime string
    time_updated str
    The time the Model was updated. An RFC3339 formatted datetime string
    total_image_count int
    Total number of training Images
    trained_duration_in_hours float
    Total hours actually used for training
    averagePrecision Number
    Average precision of the trained model
    confidenceThreshold Number
    Confidence ratio of the calculation
    id String
    The provider-assigned unique ID for this managed resource.
    lifecycleDetails String
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    metrics String
    Complete Training Metrics for successful trained model
    precision Number
    Precision of the trained model
    recall Number
    Recall of the trained model
    state String
    The current state of the Model.
    systemTags Map<String>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    testImageCount Number
    Total number of testing Images
    timeCreated String
    The time the Model was created. An RFC3339 formatted datetime string
    timeUpdated String
    The time the Model was updated. An RFC3339 formatted datetime string
    totalImageCount Number
    Total number of training Images
    trainedDurationInHours Number
    Total hours actually used for training

    Look up Existing Model Resource

    Get an existing Model 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?: ModelState, opts?: CustomResourceOptions): Model
    @staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        average_precision: Optional[float] = None,
        compartment_id: Optional[str] = None,
        confidence_threshold: Optional[float] = None,
        defined_tags: Optional[Mapping[str, str]] = None,
        description: Optional[str] = None,
        display_name: Optional[str] = None,
        freeform_tags: Optional[Mapping[str, str]] = None,
        is_quick_mode: Optional[bool] = None,
        lifecycle_details: Optional[str] = None,
        max_training_duration_in_hours: Optional[float] = None,
        metrics: Optional[str] = None,
        model_type: Optional[str] = None,
        model_version: Optional[str] = None,
        precision: Optional[float] = None,
        project_id: Optional[str] = None,
        recall: Optional[float] = None,
        state: Optional[str] = None,
        system_tags: Optional[Mapping[str, str]] = None,
        test_image_count: Optional[int] = None,
        testing_dataset: Optional[ModelTestingDatasetArgs] = None,
        time_created: Optional[str] = None,
        time_updated: Optional[str] = None,
        total_image_count: Optional[int] = None,
        trained_duration_in_hours: Optional[float] = None,
        training_dataset: Optional[ModelTrainingDatasetArgs] = None,
        validation_dataset: Optional[ModelValidationDatasetArgs] = None) -> Model
    func GetModel(ctx *Context, name string, id IDInput, state *ModelState, opts ...ResourceOption) (*Model, error)
    public static Model Get(string name, Input<string> id, ModelState? state, CustomResourceOptions? opts = null)
    public static Model get(String name, Output<String> id, ModelState state, CustomResourceOptions options)
    resources:  _:    type: oci:AiVision:Model    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:
    AveragePrecision double
    Average precision of the trained model
    CompartmentId string
    (Updatable) Compartment Identifier
    ConfidenceThreshold double
    Confidence ratio of the calculation
    DefinedTags Dictionary<string, string>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    Description string
    (Updatable) A short description of the Model.
    DisplayName string
    (Updatable) Model Identifier
    FreeformTags Dictionary<string, string>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    IsQuickMode bool
    If It's true, Training is set for recommended epochs needed for quick training.
    LifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    MaxTrainingDurationInHours double
    The maximum duration in hours for which the training will run.
    Metrics string
    Complete Training Metrics for successful trained model
    ModelType string
    The type of the model.
    ModelVersion string
    Model version.
    Precision double
    Precision of the trained model
    ProjectId string
    The OCID of the project to associate with the model.
    Recall double
    Recall of the trained model
    State string
    The current state of the Model.
    SystemTags Dictionary<string, string>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    TestImageCount int
    Total number of testing Images
    TestingDataset ModelTestingDataset
    The base entity for a Dataset, which is the input for Model creation.
    TimeCreated string
    The time the Model was created. An RFC3339 formatted datetime string
    TimeUpdated string
    The time the Model was updated. An RFC3339 formatted datetime string
    TotalImageCount int
    Total number of training Images
    TrainedDurationInHours double
    Total hours actually used for training
    TrainingDataset ModelTrainingDataset
    The base entity for a Dataset, which is the input for Model creation.
    ValidationDataset ModelValidationDataset
    The base entity for a Dataset, which is the input for Model creation.
    AveragePrecision float64
    Average precision of the trained model
    CompartmentId string
    (Updatable) Compartment Identifier
    ConfidenceThreshold float64
    Confidence ratio of the calculation
    DefinedTags map[string]string
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    Description string
    (Updatable) A short description of the Model.
    DisplayName string
    (Updatable) Model Identifier
    FreeformTags map[string]string
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    IsQuickMode bool
    If It's true, Training is set for recommended epochs needed for quick training.
    LifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    MaxTrainingDurationInHours float64
    The maximum duration in hours for which the training will run.
    Metrics string
    Complete Training Metrics for successful trained model
    ModelType string
    The type of the model.
    ModelVersion string
    Model version.
    Precision float64
    Precision of the trained model
    ProjectId string
    The OCID of the project to associate with the model.
    Recall float64
    Recall of the trained model
    State string
    The current state of the Model.
    SystemTags map[string]string
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    TestImageCount int
    Total number of testing Images
    TestingDataset ModelTestingDatasetArgs
    The base entity for a Dataset, which is the input for Model creation.
    TimeCreated string
    The time the Model was created. An RFC3339 formatted datetime string
    TimeUpdated string
    The time the Model was updated. An RFC3339 formatted datetime string
    TotalImageCount int
    Total number of training Images
    TrainedDurationInHours float64
    Total hours actually used for training
    TrainingDataset ModelTrainingDatasetArgs
    The base entity for a Dataset, which is the input for Model creation.
    ValidationDataset ModelValidationDatasetArgs
    The base entity for a Dataset, which is the input for Model creation.
    averagePrecision Double
    Average precision of the trained model
    compartmentId String
    (Updatable) Compartment Identifier
    confidenceThreshold Double
    Confidence ratio of the calculation
    definedTags Map<String,String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description String
    (Updatable) A short description of the Model.
    displayName String
    (Updatable) Model Identifier
    freeformTags Map<String,String>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    isQuickMode Boolean
    If It's true, Training is set for recommended epochs needed for quick training.
    lifecycleDetails String
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    maxTrainingDurationInHours Double
    The maximum duration in hours for which the training will run.
    metrics String
    Complete Training Metrics for successful trained model
    modelType String
    The type of the model.
    modelVersion String
    Model version.
    precision Double
    Precision of the trained model
    projectId String
    The OCID of the project to associate with the model.
    recall Double
    Recall of the trained model
    state String
    The current state of the Model.
    systemTags Map<String,String>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    testImageCount Integer
    Total number of testing Images
    testingDataset ModelTestingDataset
    The base entity for a Dataset, which is the input for Model creation.
    timeCreated String
    The time the Model was created. An RFC3339 formatted datetime string
    timeUpdated String
    The time the Model was updated. An RFC3339 formatted datetime string
    totalImageCount Integer
    Total number of training Images
    trainedDurationInHours Double
    Total hours actually used for training
    trainingDataset ModelTrainingDataset
    The base entity for a Dataset, which is the input for Model creation.
    validationDataset ModelValidationDataset
    The base entity for a Dataset, which is the input for Model creation.
    averagePrecision number
    Average precision of the trained model
    compartmentId string
    (Updatable) Compartment Identifier
    confidenceThreshold number
    Confidence ratio of the calculation
    definedTags {[key: string]: string}
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description string
    (Updatable) A short description of the Model.
    displayName string
    (Updatable) Model Identifier
    freeformTags {[key: string]: string}
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    isQuickMode boolean
    If It's true, Training is set for recommended epochs needed for quick training.
    lifecycleDetails string
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    maxTrainingDurationInHours number
    The maximum duration in hours for which the training will run.
    metrics string
    Complete Training Metrics for successful trained model
    modelType string
    The type of the model.
    modelVersion string
    Model version.
    precision number
    Precision of the trained model
    projectId string
    The OCID of the project to associate with the model.
    recall number
    Recall of the trained model
    state string
    The current state of the Model.
    systemTags {[key: string]: string}
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    testImageCount number
    Total number of testing Images
    testingDataset ModelTestingDataset
    The base entity for a Dataset, which is the input for Model creation.
    timeCreated string
    The time the Model was created. An RFC3339 formatted datetime string
    timeUpdated string
    The time the Model was updated. An RFC3339 formatted datetime string
    totalImageCount number
    Total number of training Images
    trainedDurationInHours number
    Total hours actually used for training
    trainingDataset ModelTrainingDataset
    The base entity for a Dataset, which is the input for Model creation.
    validationDataset ModelValidationDataset
    The base entity for a Dataset, which is the input for Model creation.
    average_precision float
    Average precision of the trained model
    compartment_id str
    (Updatable) Compartment Identifier
    confidence_threshold float
    Confidence ratio of the calculation
    defined_tags Mapping[str, str]
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description str
    (Updatable) A short description of the Model.
    display_name str
    (Updatable) Model Identifier
    freeform_tags Mapping[str, str]
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    is_quick_mode bool
    If It's true, Training is set for recommended epochs needed for quick training.
    lifecycle_details str
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    max_training_duration_in_hours float
    The maximum duration in hours for which the training will run.
    metrics str
    Complete Training Metrics for successful trained model
    model_type str
    The type of the model.
    model_version str
    Model version.
    precision float
    Precision of the trained model
    project_id str
    The OCID of the project to associate with the model.
    recall float
    Recall of the trained model
    state str
    The current state of the Model.
    system_tags Mapping[str, str]
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    test_image_count int
    Total number of testing Images
    testing_dataset ModelTestingDatasetArgs
    The base entity for a Dataset, which is the input for Model creation.
    time_created str
    The time the Model was created. An RFC3339 formatted datetime string
    time_updated str
    The time the Model was updated. An RFC3339 formatted datetime string
    total_image_count int
    Total number of training Images
    trained_duration_in_hours float
    Total hours actually used for training
    training_dataset ModelTrainingDatasetArgs
    The base entity for a Dataset, which is the input for Model creation.
    validation_dataset ModelValidationDatasetArgs
    The base entity for a Dataset, which is the input for Model creation.
    averagePrecision Number
    Average precision of the trained model
    compartmentId String
    (Updatable) Compartment Identifier
    confidenceThreshold Number
    Confidence ratio of the calculation
    definedTags Map<String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
    description String
    (Updatable) A short description of the Model.
    displayName String
    (Updatable) Model Identifier
    freeformTags Map<String>
    (Updatable) Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
    isQuickMode Boolean
    If It's true, Training is set for recommended epochs needed for quick training.
    lifecycleDetails String
    A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
    maxTrainingDurationInHours Number
    The maximum duration in hours for which the training will run.
    metrics String
    Complete Training Metrics for successful trained model
    modelType String
    The type of the model.
    modelVersion String
    Model version.
    precision Number
    Precision of the trained model
    projectId String
    The OCID of the project to associate with the model.
    recall Number
    Recall of the trained model
    state String
    The current state of the Model.
    systemTags Map<String>
    Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
    testImageCount Number
    Total number of testing Images
    testingDataset Property Map
    The base entity for a Dataset, which is the input for Model creation.
    timeCreated String
    The time the Model was created. An RFC3339 formatted datetime string
    timeUpdated String
    The time the Model was updated. An RFC3339 formatted datetime string
    totalImageCount Number
    Total number of training Images
    trainedDurationInHours Number
    Total hours actually used for training
    trainingDataset Property Map
    The base entity for a Dataset, which is the input for Model creation.
    validationDataset Property Map
    The base entity for a Dataset, which is the input for Model creation.

    Supporting Types

    ModelTestingDataset, ModelTestingDatasetArgs

    DatasetType string
    Type of the Dataset.
    Bucket string
    The name of the ObjectStorage bucket that contains the input data file.
    DatasetId string
    The OCID of the Data Science Labeling Dataset.
    NamespaceName string
    The namespace name of the ObjectStorage bucket that contains the input data file.
    Object string
    The object name of the input data file.
    DatasetType string
    Type of the Dataset.
    Bucket string
    The name of the ObjectStorage bucket that contains the input data file.
    DatasetId string
    The OCID of the Data Science Labeling Dataset.
    NamespaceName string
    The namespace name of the ObjectStorage bucket that contains the input data file.
    Object string
    The object name of the input data file.
    datasetType String
    Type of the Dataset.
    bucket String
    The name of the ObjectStorage bucket that contains the input data file.
    datasetId String
    The OCID of the Data Science Labeling Dataset.
    namespaceName String
    The namespace name of the ObjectStorage bucket that contains the input data file.
    object String
    The object name of the input data file.
    datasetType string
    Type of the Dataset.
    bucket string
    The name of the ObjectStorage bucket that contains the input data file.
    datasetId string
    The OCID of the Data Science Labeling Dataset.
    namespaceName string
    The namespace name of the ObjectStorage bucket that contains the input data file.
    object string
    The object name of the input data file.
    dataset_type str
    Type of the Dataset.
    bucket str
    The name of the ObjectStorage bucket that contains the input data file.
    dataset_id str
    The OCID of the Data Science Labeling Dataset.
    namespace_name str
    The namespace name of the ObjectStorage bucket that contains the input data file.
    object str
    The object name of the input data file.
    datasetType String
    Type of the Dataset.
    bucket String
    The name of the ObjectStorage bucket that contains the input data file.
    datasetId String
    The OCID of the Data Science Labeling Dataset.
    namespaceName String
    The namespace name of the ObjectStorage bucket that contains the input data file.
    object String
    The object name of the input data file.

    ModelTrainingDataset, ModelTrainingDatasetArgs

    DatasetType string
    Type of the Dataset.
    Bucket string
    The name of the ObjectStorage bucket that contains the input data file.
    DatasetId string
    The OCID of the Data Science Labeling Dataset.
    NamespaceName string
    The namespace name of the ObjectStorage bucket that contains the input data file.
    Object string
    The object name of the input data file.
    DatasetType string
    Type of the Dataset.
    Bucket string
    The name of the ObjectStorage bucket that contains the input data file.
    DatasetId string
    The OCID of the Data Science Labeling Dataset.
    NamespaceName string
    The namespace name of the ObjectStorage bucket that contains the input data file.
    Object string
    The object name of the input data file.
    datasetType String
    Type of the Dataset.
    bucket String
    The name of the ObjectStorage bucket that contains the input data file.
    datasetId String
    The OCID of the Data Science Labeling Dataset.
    namespaceName String
    The namespace name of the ObjectStorage bucket that contains the input data file.
    object String
    The object name of the input data file.
    datasetType string
    Type of the Dataset.
    bucket string
    The name of the ObjectStorage bucket that contains the input data file.
    datasetId string
    The OCID of the Data Science Labeling Dataset.
    namespaceName string
    The namespace name of the ObjectStorage bucket that contains the input data file.
    object string
    The object name of the input data file.
    dataset_type str
    Type of the Dataset.
    bucket str
    The name of the ObjectStorage bucket that contains the input data file.
    dataset_id str
    The OCID of the Data Science Labeling Dataset.
    namespace_name str
    The namespace name of the ObjectStorage bucket that contains the input data file.
    object str
    The object name of the input data file.
    datasetType String
    Type of the Dataset.
    bucket String
    The name of the ObjectStorage bucket that contains the input data file.
    datasetId String
    The OCID of the Data Science Labeling Dataset.
    namespaceName String
    The namespace name of the ObjectStorage bucket that contains the input data file.
    object String
    The object name of the input data file.

    ModelValidationDataset, ModelValidationDatasetArgs

    DatasetType string
    Type of the Dataset.
    Bucket string
    The name of the ObjectStorage bucket that contains the input data file.
    DatasetId string
    The OCID of the Data Science Labeling Dataset.
    NamespaceName string
    The namespace name of the ObjectStorage bucket that contains the input data file.
    Object string

    The object name of the input data file.

    ** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

    DatasetType string
    Type of the Dataset.
    Bucket string
    The name of the ObjectStorage bucket that contains the input data file.
    DatasetId string
    The OCID of the Data Science Labeling Dataset.
    NamespaceName string
    The namespace name of the ObjectStorage bucket that contains the input data file.
    Object string

    The object name of the input data file.

    ** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

    datasetType String
    Type of the Dataset.
    bucket String
    The name of the ObjectStorage bucket that contains the input data file.
    datasetId String
    The OCID of the Data Science Labeling Dataset.
    namespaceName String
    The namespace name of the ObjectStorage bucket that contains the input data file.
    object String

    The object name of the input data file.

    ** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

    datasetType string
    Type of the Dataset.
    bucket string
    The name of the ObjectStorage bucket that contains the input data file.
    datasetId string
    The OCID of the Data Science Labeling Dataset.
    namespaceName string
    The namespace name of the ObjectStorage bucket that contains the input data file.
    object string

    The object name of the input data file.

    ** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

    dataset_type str
    Type of the Dataset.
    bucket str
    The name of the ObjectStorage bucket that contains the input data file.
    dataset_id str
    The OCID of the Data Science Labeling Dataset.
    namespace_name str
    The namespace name of the ObjectStorage bucket that contains the input data file.
    object str

    The object name of the input data file.

    ** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

    datasetType String
    Type of the Dataset.
    bucket String
    The name of the ObjectStorage bucket that contains the input data file.
    datasetId String
    The OCID of the Data Science Labeling Dataset.
    namespaceName String
    The namespace name of the ObjectStorage bucket that contains the input data file.
    object String

    The object name of the input data file.

    ** IMPORTANT ** Any change to a property that does not support update will force the destruction and recreation of the resource with the new property values

    Import

    Models can be imported using the id, e.g.

    $ pulumi import oci:AiVision/model:Model test_model "id"

    To learn more about importing existing cloud resources, see Importing resources.

    Package Details

    Repository
    oci pulumi/pulumi-oci
    License
    Apache-2.0
    Notes
    This Pulumi package is based on the oci Terraform Provider.
    oci logo
    Oracle Cloud Infrastructure v2.33.0 published on Thursday, May 1, 2025 by Pulumi
    pulumi/pulumi-oci