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Oracle Cloud Infrastructure v3.7.0 published on Saturday, Sep 13, 2025 by Pulumi
pulumi/pulumi-oci

oci.AiVision.Model

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Oracle Cloud Infrastructure v3.7.0 published on Saturday, Sep 13, 2025 by Pulumi
pulumi/pulumi-oci

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

    Create 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: modelDefinedTags,
    description: modelDescription,
    displayName: modelDisplayName,
    freeformTags: modelFreeformTags,
    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.aivision.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=model_defined_tags,
    description=model_description,
    display_name=model_display_name,
    freeform_tags=model_freeform_tags,
    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/v3/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.Any(modelDefinedTags),
			Description:                pulumi.Any(modelDescription),
			DisplayName:                pulumi.Any(modelDisplayName),
			FreeformTags:               pulumi.Any(modelFreeformTags),
			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 = modelDefinedTags,
        Description = modelDescription,
        DisplayName = modelDisplayName,
        FreeformTags = modelFreeformTags,
        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(modelDefinedTags)
            .description(modelDescription)
            .displayName(modelDisplayName)
            .freeformTags(modelFreeformTags)
            .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: ${modelDefinedTags}
      description: ${modelDescription}
      displayName: ${modelDisplayName}
      freeformTags: ${modelFreeformTags}
      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.aivision.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) The compartment identifier.
    ModelType string
    Which type of Vision model this is.
    ProjectId string
    The OCID of the project that contains the model.
    TrainingDataset ModelTrainingDataset
    The base entity which is the input for creating and training a model.
    DefinedTags Dictionary<string, string>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    Description string
    (Updatable) An optional description of the model.
    DisplayName string
    (Updatable) A human-friendly name for the model, which can be changed.
    FreeformTags Dictionary<string, string>
    (Updatable) A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    IsQuickMode bool
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    MaxTrainingDurationInHours double
    The maximum model training duration in hours, expressed as a decimal fraction.
    ModelVersion string
    The model version
    TestingDataset ModelTestingDataset
    The base entity which is the input for creating and training a model.
    ValidationDataset ModelValidationDataset
    The base entity which is the input for creating and training a model.
    CompartmentId string
    (Updatable) The compartment identifier.
    ModelType string
    Which type of Vision model this is.
    ProjectId string
    The OCID of the project that contains the model.
    TrainingDataset ModelTrainingDatasetArgs
    The base entity which is the input for creating and training a model.
    DefinedTags map[string]string
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    Description string
    (Updatable) An optional description of the model.
    DisplayName string
    (Updatable) A human-friendly name for the model, which can be changed.
    FreeformTags map[string]string
    (Updatable) A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    IsQuickMode bool
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    MaxTrainingDurationInHours float64
    The maximum model training duration in hours, expressed as a decimal fraction.
    ModelVersion string
    The model version
    TestingDataset ModelTestingDatasetArgs
    The base entity which is the input for creating and training a model.
    ValidationDataset ModelValidationDatasetArgs
    The base entity which is the input for creating and training a model.
    compartmentId String
    (Updatable) The compartment identifier.
    modelType String
    Which type of Vision model this is.
    projectId String
    The OCID of the project that contains the model.
    trainingDataset ModelTrainingDataset
    The base entity which is the input for creating and training a model.
    definedTags Map<String,String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description String
    (Updatable) An optional description of the model.
    displayName String
    (Updatable) A human-friendly name for the model, which can be changed.
    freeformTags Map<String,String>
    (Updatable) A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    isQuickMode Boolean
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    maxTrainingDurationInHours Double
    The maximum model training duration in hours, expressed as a decimal fraction.
    modelVersion String
    The model version
    testingDataset ModelTestingDataset
    The base entity which is the input for creating and training a model.
    validationDataset ModelValidationDataset
    The base entity which is the input for creating and training a model.
    compartmentId string
    (Updatable) The compartment identifier.
    modelType string
    Which type of Vision model this is.
    projectId string
    The OCID of the project that contains the model.
    trainingDataset ModelTrainingDataset
    The base entity which is the input for creating and training a model.
    definedTags {[key: string]: string}
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description string
    (Updatable) An optional description of the model.
    displayName string
    (Updatable) A human-friendly name for the model, which can be changed.
    freeformTags {[key: string]: string}
    (Updatable) A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    isQuickMode boolean
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    maxTrainingDurationInHours number
    The maximum model training duration in hours, expressed as a decimal fraction.
    modelVersion string
    The model version
    testingDataset ModelTestingDataset
    The base entity which is the input for creating and training a model.
    validationDataset ModelValidationDataset
    The base entity which is the input for creating and training a model.
    compartment_id str
    (Updatable) The compartment identifier.
    model_type str
    Which type of Vision model this is.
    project_id str
    The OCID of the project that contains the model.
    training_dataset ModelTrainingDatasetArgs
    The base entity which is the input for creating and training a model.
    defined_tags Mapping[str, str]
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description str
    (Updatable) An optional description of the model.
    display_name str
    (Updatable) A human-friendly name for the model, which can be changed.
    freeform_tags Mapping[str, str]
    (Updatable) A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    is_quick_mode bool
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    max_training_duration_in_hours float
    The maximum model training duration in hours, expressed as a decimal fraction.
    model_version str
    The model version
    testing_dataset ModelTestingDatasetArgs
    The base entity which is the input for creating and training a model.
    validation_dataset ModelValidationDatasetArgs
    The base entity which is the input for creating and training a model.
    compartmentId String
    (Updatable) The compartment identifier.
    modelType String
    Which type of Vision model this is.
    projectId String
    The OCID of the project that contains the model.
    trainingDataset Property Map
    The base entity which is the input for creating and training a model.
    definedTags Map<String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description String
    (Updatable) An optional description of the model.
    displayName String
    (Updatable) A human-friendly name for the model, which can be changed.
    freeformTags Map<String>
    (Updatable) A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    isQuickMode Boolean
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    maxTrainingDurationInHours Number
    The maximum model training duration in hours, expressed as a decimal fraction.
    modelVersion String
    The model version
    testingDataset Property Map
    The base entity which is the input for creating and training a model.
    validationDataset Property Map
    The base entity which is the input for creating and training a model.

    Outputs

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

    AveragePrecision double
    The mean average precision of the trained model.
    ConfidenceThreshold double
    The intersection over the union threshold used for calculating precision and recall.
    Id string
    The provider-assigned unique ID for this managed resource.
    LifecycleDetails string
    A message describing the current state in more detail, that can provide actionable information if training failed.
    Metrics string
    The complete set of per-label metrics for successfully trained models.
    Precision double
    The 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. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    TestImageCount int
    The number of images set aside for evaluating model performance metrics after training.
    TimeCreated string
    When the model was created, as an RFC3339 datetime string.
    TimeUpdated string
    When the model was updated, as an RFC3339 datetime string.
    TotalImageCount int
    The number of images in the dataset used to train, validate, and test the model.
    TrainedDurationInHours double
    The total hours actually used for model training.
    AveragePrecision float64
    The mean average precision of the trained model.
    ConfidenceThreshold float64
    The intersection over the union threshold used for calculating precision and recall.
    Id string
    The provider-assigned unique ID for this managed resource.
    LifecycleDetails string
    A message describing the current state in more detail, that can provide actionable information if training failed.
    Metrics string
    The complete set of per-label metrics for successfully trained models.
    Precision float64
    The 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. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    TestImageCount int
    The number of images set aside for evaluating model performance metrics after training.
    TimeCreated string
    When the model was created, as an RFC3339 datetime string.
    TimeUpdated string
    When the model was updated, as an RFC3339 datetime string.
    TotalImageCount int
    The number of images in the dataset used to train, validate, and test the model.
    TrainedDurationInHours float64
    The total hours actually used for model training.
    averagePrecision Double
    The mean average precision of the trained model.
    confidenceThreshold Double
    The intersection over the union threshold used for calculating precision and recall.
    id String
    The provider-assigned unique ID for this managed resource.
    lifecycleDetails String
    A message describing the current state in more detail, that can provide actionable information if training failed.
    metrics String
    The complete set of per-label metrics for successfully trained models.
    precision Double
    The 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. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    testImageCount Integer
    The number of images set aside for evaluating model performance metrics after training.
    timeCreated String
    When the model was created, as an RFC3339 datetime string.
    timeUpdated String
    When the model was updated, as an RFC3339 datetime string.
    totalImageCount Integer
    The number of images in the dataset used to train, validate, and test the model.
    trainedDurationInHours Double
    The total hours actually used for model training.
    averagePrecision number
    The mean average precision of the trained model.
    confidenceThreshold number
    The intersection over the union threshold used for calculating precision and recall.
    id string
    The provider-assigned unique ID for this managed resource.
    lifecycleDetails string
    A message describing the current state in more detail, that can provide actionable information if training failed.
    metrics string
    The complete set of per-label metrics for successfully trained models.
    precision number
    The 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. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    testImageCount number
    The number of images set aside for evaluating model performance metrics after training.
    timeCreated string
    When the model was created, as an RFC3339 datetime string.
    timeUpdated string
    When the model was updated, as an RFC3339 datetime string.
    totalImageCount number
    The number of images in the dataset used to train, validate, and test the model.
    trainedDurationInHours number
    The total hours actually used for model training.
    average_precision float
    The mean average precision of the trained model.
    confidence_threshold float
    The intersection over the union threshold used for calculating precision and recall.
    id str
    The provider-assigned unique ID for this managed resource.
    lifecycle_details str
    A message describing the current state in more detail, that can provide actionable information if training failed.
    metrics str
    The complete set of per-label metrics for successfully trained models.
    precision float
    The 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. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    test_image_count int
    The number of images set aside for evaluating model performance metrics after training.
    time_created str
    When the model was created, as an RFC3339 datetime string.
    time_updated str
    When the model was updated, as an RFC3339 datetime string.
    total_image_count int
    The number of images in the dataset used to train, validate, and test the model.
    trained_duration_in_hours float
    The total hours actually used for model training.
    averagePrecision Number
    The mean average precision of the trained model.
    confidenceThreshold Number
    The intersection over the union threshold used for calculating precision and recall.
    id String
    The provider-assigned unique ID for this managed resource.
    lifecycleDetails String
    A message describing the current state in more detail, that can provide actionable information if training failed.
    metrics String
    The complete set of per-label metrics for successfully trained models.
    precision Number
    The 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. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    testImageCount Number
    The number of images set aside for evaluating model performance metrics after training.
    timeCreated String
    When the model was created, as an RFC3339 datetime string.
    timeUpdated String
    When the model was updated, as an RFC3339 datetime string.
    totalImageCount Number
    The number of images in the dataset used to train, validate, and test the model.
    trainedDurationInHours Number
    The total hours actually used for model 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
    The mean average precision of the trained model.
    CompartmentId string
    (Updatable) The compartment identifier.
    ConfidenceThreshold double
    The intersection over the union threshold used for calculating precision and recall.
    DefinedTags Dictionary<string, string>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    Description string
    (Updatable) An optional description of the model.
    DisplayName string
    (Updatable) A human-friendly name for the model, which can be changed.
    FreeformTags Dictionary<string, string>
    (Updatable) A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    IsQuickMode bool
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    LifecycleDetails string
    A message describing the current state in more detail, that can provide actionable information if training failed.
    MaxTrainingDurationInHours double
    The maximum model training duration in hours, expressed as a decimal fraction.
    Metrics string
    The complete set of per-label metrics for successfully trained models.
    ModelType string
    Which type of Vision model this is.
    ModelVersion string
    The model version
    Precision double
    The precision of the trained model.
    ProjectId string
    The OCID of the project that contains 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. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    TestImageCount int
    The number of images set aside for evaluating model performance metrics after training.
    TestingDataset ModelTestingDataset
    The base entity which is the input for creating and training a model.
    TimeCreated string
    When the model was created, as an RFC3339 datetime string.
    TimeUpdated string
    When the model was updated, as an RFC3339 datetime string.
    TotalImageCount int
    The number of images in the dataset used to train, validate, and test the model.
    TrainedDurationInHours double
    The total hours actually used for model training.
    TrainingDataset ModelTrainingDataset
    The base entity which is the input for creating and training a model.
    ValidationDataset ModelValidationDataset
    The base entity which is the input for creating and training a model.
    AveragePrecision float64
    The mean average precision of the trained model.
    CompartmentId string
    (Updatable) The compartment identifier.
    ConfidenceThreshold float64
    The intersection over the union threshold used for calculating precision and recall.
    DefinedTags map[string]string
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    Description string
    (Updatable) An optional description of the model.
    DisplayName string
    (Updatable) A human-friendly name for the model, which can be changed.
    FreeformTags map[string]string
    (Updatable) A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    IsQuickMode bool
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    LifecycleDetails string
    A message describing the current state in more detail, that can provide actionable information if training failed.
    MaxTrainingDurationInHours float64
    The maximum model training duration in hours, expressed as a decimal fraction.
    Metrics string
    The complete set of per-label metrics for successfully trained models.
    ModelType string
    Which type of Vision model this is.
    ModelVersion string
    The model version
    Precision float64
    The precision of the trained model.
    ProjectId string
    The OCID of the project that contains 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. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    TestImageCount int
    The number of images set aside for evaluating model performance metrics after training.
    TestingDataset ModelTestingDatasetArgs
    The base entity which is the input for creating and training a model.
    TimeCreated string
    When the model was created, as an RFC3339 datetime string.
    TimeUpdated string
    When the model was updated, as an RFC3339 datetime string.
    TotalImageCount int
    The number of images in the dataset used to train, validate, and test the model.
    TrainedDurationInHours float64
    The total hours actually used for model training.
    TrainingDataset ModelTrainingDatasetArgs
    The base entity which is the input for creating and training a model.
    ValidationDataset ModelValidationDatasetArgs
    The base entity which is the input for creating and training a model.
    averagePrecision Double
    The mean average precision of the trained model.
    compartmentId String
    (Updatable) The compartment identifier.
    confidenceThreshold Double
    The intersection over the union threshold used for calculating precision and recall.
    definedTags Map<String,String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description String
    (Updatable) An optional description of the model.
    displayName String
    (Updatable) A human-friendly name for the model, which can be changed.
    freeformTags Map<String,String>
    (Updatable) A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    isQuickMode Boolean
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    lifecycleDetails String
    A message describing the current state in more detail, that can provide actionable information if training failed.
    maxTrainingDurationInHours Double
    The maximum model training duration in hours, expressed as a decimal fraction.
    metrics String
    The complete set of per-label metrics for successfully trained models.
    modelType String
    Which type of Vision model this is.
    modelVersion String
    The model version
    precision Double
    The precision of the trained model.
    projectId String
    The OCID of the project that contains 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. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    testImageCount Integer
    The number of images set aside for evaluating model performance metrics after training.
    testingDataset ModelTestingDataset
    The base entity which is the input for creating and training a model.
    timeCreated String
    When the model was created, as an RFC3339 datetime string.
    timeUpdated String
    When the model was updated, as an RFC3339 datetime string.
    totalImageCount Integer
    The number of images in the dataset used to train, validate, and test the model.
    trainedDurationInHours Double
    The total hours actually used for model training.
    trainingDataset ModelTrainingDataset
    The base entity which is the input for creating and training a model.
    validationDataset ModelValidationDataset
    The base entity which is the input for creating and training a model.
    averagePrecision number
    The mean average precision of the trained model.
    compartmentId string
    (Updatable) The compartment identifier.
    confidenceThreshold number
    The intersection over the union threshold used for calculating precision and recall.
    definedTags {[key: string]: string}
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description string
    (Updatable) An optional description of the model.
    displayName string
    (Updatable) A human-friendly name for the model, which can be changed.
    freeformTags {[key: string]: string}
    (Updatable) A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    isQuickMode boolean
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    lifecycleDetails string
    A message describing the current state in more detail, that can provide actionable information if training failed.
    maxTrainingDurationInHours number
    The maximum model training duration in hours, expressed as a decimal fraction.
    metrics string
    The complete set of per-label metrics for successfully trained models.
    modelType string
    Which type of Vision model this is.
    modelVersion string
    The model version
    precision number
    The precision of the trained model.
    projectId string
    The OCID of the project that contains 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. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    testImageCount number
    The number of images set aside for evaluating model performance metrics after training.
    testingDataset ModelTestingDataset
    The base entity which is the input for creating and training a model.
    timeCreated string
    When the model was created, as an RFC3339 datetime string.
    timeUpdated string
    When the model was updated, as an RFC3339 datetime string.
    totalImageCount number
    The number of images in the dataset used to train, validate, and test the model.
    trainedDurationInHours number
    The total hours actually used for model training.
    trainingDataset ModelTrainingDataset
    The base entity which is the input for creating and training a model.
    validationDataset ModelValidationDataset
    The base entity which is the input for creating and training a model.
    average_precision float
    The mean average precision of the trained model.
    compartment_id str
    (Updatable) The compartment identifier.
    confidence_threshold float
    The intersection over the union threshold used for calculating precision and recall.
    defined_tags Mapping[str, str]
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description str
    (Updatable) An optional description of the model.
    display_name str
    (Updatable) A human-friendly name for the model, which can be changed.
    freeform_tags Mapping[str, str]
    (Updatable) A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    is_quick_mode bool
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    lifecycle_details str
    A message describing the current state in more detail, that can provide actionable information if training failed.
    max_training_duration_in_hours float
    The maximum model training duration in hours, expressed as a decimal fraction.
    metrics str
    The complete set of per-label metrics for successfully trained models.
    model_type str
    Which type of Vision model this is.
    model_version str
    The model version
    precision float
    The precision of the trained model.
    project_id str
    The OCID of the project that contains 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. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    test_image_count int
    The number of images set aside for evaluating model performance metrics after training.
    testing_dataset ModelTestingDatasetArgs
    The base entity which is the input for creating and training a model.
    time_created str
    When the model was created, as an RFC3339 datetime string.
    time_updated str
    When the model was updated, as an RFC3339 datetime string.
    total_image_count int
    The number of images in the dataset used to train, validate, and test the model.
    trained_duration_in_hours float
    The total hours actually used for model training.
    training_dataset ModelTrainingDatasetArgs
    The base entity which is the input for creating and training a model.
    validation_dataset ModelValidationDatasetArgs
    The base entity which is the input for creating and training a model.
    averagePrecision Number
    The mean average precision of the trained model.
    compartmentId String
    (Updatable) The compartment identifier.
    confidenceThreshold Number
    The intersection over the union threshold used for calculating precision and recall.
    definedTags Map<String>
    (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
    description String
    (Updatable) An optional description of the model.
    displayName String
    (Updatable) A human-friendly name for the model, which can be changed.
    freeformTags Map<String>
    (Updatable) A simple key-value pair that is applied without any predefined name, type, or scope. It exists for cross-compatibility only. For example: {"bar-key": "value"}
    isQuickMode Boolean
    Set to true when experimenting with a new model type or dataset, so the model training is quick, with a predefined low number of passes through the training data.
    lifecycleDetails String
    A message describing the current state in more detail, that can provide actionable information if training failed.
    maxTrainingDurationInHours Number
    The maximum model training duration in hours, expressed as a decimal fraction.
    metrics String
    The complete set of per-label metrics for successfully trained models.
    modelType String
    Which type of Vision model this is.
    modelVersion String
    The model version
    precision Number
    The precision of the trained model.
    projectId String
    The OCID of the project that contains 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. For example: {"orcl-cloud": {"free-tier-retained": "true"}}
    testImageCount Number
    The number of images set aside for evaluating model performance metrics after training.
    testingDataset Property Map
    The base entity which is the input for creating and training a model.
    timeCreated String
    When the model was created, as an RFC3339 datetime string.
    timeUpdated String
    When the model was updated, as an RFC3339 datetime string.
    totalImageCount Number
    The number of images in the dataset used to train, validate, and test the model.
    trainedDurationInHours Number
    The total hours actually used for model training.
    trainingDataset Property Map
    The base entity which is the input for creating and training a model.
    validationDataset Property Map
    The base entity which is the input for creating and training a model.

    Supporting Types

    ModelTestingDataset, ModelTestingDatasetArgs

    DatasetType string
    The dataset type, based on where it is stored.
    Bucket string
    The name of the Object Storage bucket that contains the input data file.
    DatasetId string
    OCID of the Data Labeling dataset.
    NamespaceName string
    Object string
    The object name of the input data file.
    DatasetType string
    The dataset type, based on where it is stored.
    Bucket string
    The name of the Object Storage bucket that contains the input data file.
    DatasetId string
    OCID of the Data Labeling dataset.
    NamespaceName string
    Object string
    The object name of the input data file.
    datasetType String
    The dataset type, based on where it is stored.
    bucket String
    The name of the Object Storage bucket that contains the input data file.
    datasetId String
    OCID of the Data Labeling dataset.
    namespaceName String
    object String
    The object name of the input data file.
    datasetType string
    The dataset type, based on where it is stored.
    bucket string
    The name of the Object Storage bucket that contains the input data file.
    datasetId string
    OCID of the Data Labeling dataset.
    namespaceName string
    object string
    The object name of the input data file.
    dataset_type str
    The dataset type, based on where it is stored.
    bucket str
    The name of the Object Storage bucket that contains the input data file.
    dataset_id str
    OCID of the Data Labeling dataset.
    namespace_name str
    object str
    The object name of the input data file.
    datasetType String
    The dataset type, based on where it is stored.
    bucket String
    The name of the Object Storage bucket that contains the input data file.
    datasetId String
    OCID of the Data Labeling dataset.
    namespaceName String
    object String
    The object name of the input data file.

    ModelTrainingDataset, ModelTrainingDatasetArgs

    DatasetType string
    The dataset type, based on where it is stored.
    Bucket string
    The name of the Object Storage bucket that contains the input data file.
    DatasetId string
    OCID of the Data Labeling dataset.
    NamespaceName string
    Object string
    The object name of the input data file.
    DatasetType string
    The dataset type, based on where it is stored.
    Bucket string
    The name of the Object Storage bucket that contains the input data file.
    DatasetId string
    OCID of the Data Labeling dataset.
    NamespaceName string
    Object string
    The object name of the input data file.
    datasetType String
    The dataset type, based on where it is stored.
    bucket String
    The name of the Object Storage bucket that contains the input data file.
    datasetId String
    OCID of the Data Labeling dataset.
    namespaceName String
    object String
    The object name of the input data file.
    datasetType string
    The dataset type, based on where it is stored.
    bucket string
    The name of the Object Storage bucket that contains the input data file.
    datasetId string
    OCID of the Data Labeling dataset.
    namespaceName string
    object string
    The object name of the input data file.
    dataset_type str
    The dataset type, based on where it is stored.
    bucket str
    The name of the Object Storage bucket that contains the input data file.
    dataset_id str
    OCID of the Data Labeling dataset.
    namespace_name str
    object str
    The object name of the input data file.
    datasetType String
    The dataset type, based on where it is stored.
    bucket String
    The name of the Object Storage bucket that contains the input data file.
    datasetId String
    OCID of the Data Labeling dataset.
    namespaceName String
    object String
    The object name of the input data file.

    ModelValidationDataset, ModelValidationDatasetArgs

    DatasetType string
    The dataset type, based on where it is stored.
    Bucket string
    The name of the Object Storage bucket that contains the input data file.
    DatasetId string
    OCID of the Data Labeling dataset.
    NamespaceName string
    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
    The dataset type, based on where it is stored.
    Bucket string
    The name of the Object Storage bucket that contains the input data file.
    DatasetId string
    OCID of the Data Labeling dataset.
    NamespaceName string
    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
    The dataset type, based on where it is stored.
    bucket String
    The name of the Object Storage bucket that contains the input data file.
    datasetId String
    OCID of the Data Labeling dataset.
    namespaceName String
    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
    The dataset type, based on where it is stored.
    bucket string
    The name of the Object Storage bucket that contains the input data file.
    datasetId string
    OCID of the Data Labeling dataset.
    namespaceName string
    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
    The dataset type, based on where it is stored.
    bucket str
    The name of the Object Storage bucket that contains the input data file.
    dataset_id str
    OCID of the Data Labeling dataset.
    namespace_name str
    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
    The dataset type, based on where it is stored.
    bucket String
    The name of the Object Storage bucket that contains the input data file.
    datasetId String
    OCID of the Data Labeling dataset.
    namespaceName String
    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 v3.7.0 published on Saturday, Sep 13, 2025 by Pulumi
    pulumi/pulumi-oci