Oracle Cloud Infrastructure v3.9.0, Sep 24 25

Oracle Cloud Infrastructure v3.9.0 published on Wednesday, Sep 24, 2025 by Pulumi

oci.AiVision.getModels

Oracle Cloud Infrastructure v3.9.0 published on Wednesday, Sep 24, 2025 by Pulumi

Example Usage

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

const testModels = oci.AiVision.getModels({
    compartmentId: compartmentId,
    displayName: modelDisplayName,
    id: modelId,
    projectId: testProject.id,
    state: modelState,
});

import pulumi
import pulumi_oci as oci

test_models = oci.AiVision.get_models(compartment_id=compartment_id,
    display_name=model_display_name,
    id=model_id,
    project_id=test_project["id"],
    state=model_state)

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.GetModels(ctx, &aivision.GetModelsArgs{
			CompartmentId: pulumi.StringRef(compartmentId),
			DisplayName:   pulumi.StringRef(modelDisplayName),
			Id:            pulumi.StringRef(modelId),
			ProjectId:     pulumi.StringRef(testProject.Id),
			State:         pulumi.StringRef(modelState),
		}, nil)
		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 testModels = Oci.AiVision.GetModels.Invoke(new()
    {
        CompartmentId = compartmentId,
        DisplayName = modelDisplayName,
        Id = modelId,
        ProjectId = testProject.Id,
        State = modelState,
    });

});

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.oci.AiVision.AiVisionFunctions;
import com.pulumi.oci.AiVision.inputs.GetModelsArgs;
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) {
        final var testModels = AiVisionFunctions.getModels(GetModelsArgs.builder()
            .compartmentId(compartmentId)
            .displayName(modelDisplayName)
            .id(modelId)
            .projectId(testProject.id())
            .state(modelState)
            .build());

    }
}

variables:
  testModels:
    fn::invoke:
      function: oci:AiVision:getModels
      arguments:
        compartmentId: ${compartmentId}
        displayName: ${modelDisplayName}
        id: ${modelId}
        projectId: ${testProject.id}
        state: ${modelState}

Using getModels

Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

function getModels(args: GetModelsArgs, opts?: InvokeOptions): Promise<GetModelsResult>
function getModelsOutput(args: GetModelsOutputArgs, opts?: InvokeOptions): Output<GetModelsResult>

def get_models(compartment_id: Optional[str] = None,
               display_name: Optional[str] = None,
               filters: Optional[Sequence[GetModelsFilter]] = None,
               id: Optional[str] = None,
               project_id: Optional[str] = None,
               state: Optional[str] = None,
               opts: Optional[InvokeOptions] = None) -> GetModelsResult
def get_models_output(compartment_id: Optional[pulumi.Input[str]] = None,
               display_name: Optional[pulumi.Input[str]] = None,
               filters: Optional[pulumi.Input[Sequence[pulumi.Input[GetModelsFilterArgs]]]] = None,
               id: Optional[pulumi.Input[str]] = None,
               project_id: Optional[pulumi.Input[str]] = None,
               state: Optional[pulumi.Input[str]] = None,
               opts: Optional[InvokeOptions] = None) -> Output[GetModelsResult]

func GetModels(ctx *Context, args *GetModelsArgs, opts ...InvokeOption) (*GetModelsResult, error)
func GetModelsOutput(ctx *Context, args *GetModelsOutputArgs, opts ...InvokeOption) GetModelsResultOutput

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

public static class GetModels 
{
    public static Task<GetModelsResult> InvokeAsync(GetModelsArgs args, InvokeOptions? opts = null)
    public static Output<GetModelsResult> Invoke(GetModelsInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetModelsResult> getModels(GetModelsArgs args, InvokeOptions options)
public static Output<GetModelsResult> getModels(GetModelsArgs args, InvokeOptions options)

fn::invoke:
  function: oci:AiVision/getModels:getModels
  arguments:
    # arguments dictionary

The following arguments are supported:

CompartmentId string: The ID of the compartment in which to list resources.
DisplayName string: A filter to return only resources that match the entire display name given.
Filters List<GetModelsFilter>
Id string: The filter to find the model with the given identifier.
ProjectId string: The ID of the project for which to list the objects.
State string: The filter to match models with the given lifecycleState.

CompartmentId string: The ID of the compartment in which to list resources.
DisplayName string: A filter to return only resources that match the entire display name given.
Filters []GetModelsFilter
Id string: The filter to find the model with the given identifier.
ProjectId string: The ID of the project for which to list the objects.
State string: The filter to match models with the given lifecycleState.

compartmentId String: The ID of the compartment in which to list resources.
displayName String: A filter to return only resources that match the entire display name given.
filters List<GetModelsFilter>
id String: The filter to find the model with the given identifier.
projectId String: The ID of the project for which to list the objects.
state String: The filter to match models with the given lifecycleState.

compartmentId string: The ID of the compartment in which to list resources.
displayName string: A filter to return only resources that match the entire display name given.
filters GetModelsFilter[]
id string: The filter to find the model with the given identifier.
projectId string: The ID of the project for which to list the objects.
state string: The filter to match models with the given lifecycleState.

compartment_id str: The ID of the compartment in which to list resources.
display_name str: A filter to return only resources that match the entire display name given.
filters Sequence[GetModelsFilter]
id str: The filter to find the model with the given identifier.
project_id str: The ID of the project for which to list the objects.
state str: The filter to match models with the given lifecycleState.

compartmentId String: The ID of the compartment in which to list resources.
displayName String: A filter to return only resources that match the entire display name given.
filters List<Property Map>
id String: The filter to find the model with the given identifier.
projectId String: The ID of the project for which to list the objects.
state String: The filter to match models with the given lifecycleState.

getModels Result

The following output properties are available:

ModelCollections List<GetModelsModelCollection>: The list of model_collection.
CompartmentId string: The compartment identifier.
DisplayName string: A human-friendly name for the model, which can be changed.
Filters List<GetModelsFilter>
Id string: A unique identifier that is immutable after creation.
ProjectId string: The OCID of the project that contains the model.
State string: The current state of the model.

ModelCollections []GetModelsModelCollection: The list of model_collection.
CompartmentId string: The compartment identifier.
DisplayName string: A human-friendly name for the model, which can be changed.
Filters []GetModelsFilter
Id string: A unique identifier that is immutable after creation.
ProjectId string: The OCID of the project that contains the model.
State string: The current state of the model.

modelCollections List<GetModelsModelCollection>: The list of model_collection.
compartmentId String: The compartment identifier.
displayName String: A human-friendly name for the model, which can be changed.
filters List<GetModelsFilter>
id String: A unique identifier that is immutable after creation.
projectId String: The OCID of the project that contains the model.
state String: The current state of the model.

modelCollections GetModelsModelCollection[]: The list of model_collection.
compartmentId string: The compartment identifier.
displayName string: A human-friendly name for the model, which can be changed.
filters GetModelsFilter[]
id string: A unique identifier that is immutable after creation.
projectId string: The OCID of the project that contains the model.
state string: The current state of the model.

model_collections Sequence[GetModelsModelCollection]: The list of model_collection.
compartment_id str: The compartment identifier.
display_name str: A human-friendly name for the model, which can be changed.
filters Sequence[GetModelsFilter]
id str: A unique identifier that is immutable after creation.
project_id str: The OCID of the project that contains the model.
state str: The current state of the model.

modelCollections List<Property Map>: The list of model_collection.
compartmentId String: The compartment identifier.
displayName String: A human-friendly name for the model, which can be changed.
filters List<Property Map>
id String: A unique identifier that is immutable after creation.
projectId String: The OCID of the project that contains the model.
state String: The current state of the model.

Supporting Types

GetModelsFilter

Name string
Values List<string>
Regex bool

Name string
Values []string
Regex bool

name String
values List<String>
regex Boolean

name string
values string[]
regex boolean

name str
values Sequence[str]
regex bool

name String
values List<String>
regex Boolean

GetModelsModelCollection

Items List<GetModelsModelCollectionItem>

Items []GetModelsModelCollectionItem

items List<GetModelsModelCollectionItem>

items GetModelsModelCollectionItem[]

items Sequence[GetModelsModelCollectionItem]

items List<Property Map>

GetModelsModelCollectionItem

AveragePrecision double: The mean average precision of the trained model.
CompartmentId string: The ID of the compartment in which to list resources.
ConfidenceThreshold double: The intersection over the union threshold used for calculating precision and recall.
DefinedTags Dictionary<string, string>: Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
Description string: An optional description of the model.
DisplayName string: A filter to return only resources that match the entire display name given.
FreeformTags Dictionary<string, string>: 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"}
Id string: The filter to find the model with the given identifier.
IsQuickMode bool: Set to true when experimenting with a new model type or dataset, so 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: What type of Vision model this is.
ModelVersion string: The version of the model.
Precision double: The precision of the trained model.
ProjectId string: The ID of the project for which to list the objects.
Recall double: Recall of the trained model.
State string: The filter to match models with the given lifecycleState.
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.
TestingDatasets List<GetModelsModelCollectionItemTestingDataset>: 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.
TrainingDatasets List<GetModelsModelCollectionItemTrainingDataset>: The base entity which is the input for creating and training a model.
ValidationDatasets List<GetModelsModelCollectionItemValidationDataset>: 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: The ID of the compartment in which to list resources.
ConfidenceThreshold float64: The intersection over the union threshold used for calculating precision and recall.
DefinedTags map[string]string: Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
Description string: An optional description of the model.
DisplayName string: A filter to return only resources that match the entire display name given.
FreeformTags map[string]string: 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"}
Id string: The filter to find the model with the given identifier.
IsQuickMode bool: Set to true when experimenting with a new model type or dataset, so 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: What type of Vision model this is.
ModelVersion string: The version of the model.
Precision float64: The precision of the trained model.
ProjectId string: The ID of the project for which to list the objects.
Recall float64: Recall of the trained model.
State string: The filter to match models with the given lifecycleState.
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.
TestingDatasets []GetModelsModelCollectionItemTestingDataset: 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.
TrainingDatasets []GetModelsModelCollectionItemTrainingDataset: The base entity which is the input for creating and training a model.
ValidationDatasets []GetModelsModelCollectionItemValidationDataset: 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: The ID of the compartment in which to list resources.
confidenceThreshold Double: The intersection over the union threshold used for calculating precision and recall.
definedTags Map<String,String>: Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
description String: An optional description of the model.
displayName String: A filter to return only resources that match the entire display name given.
freeformTags Map<String,String>: 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"}
id String: The filter to find the model with the given identifier.
isQuickMode Boolean: Set to true when experimenting with a new model type or dataset, so 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: What type of Vision model this is.
modelVersion String: The version of the model.
precision Double: The precision of the trained model.
projectId String: The ID of the project for which to list the objects.
recall Double: Recall of the trained model.
state String: The filter to match models with the given lifecycleState.
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.
testingDatasets List<GetModelsModelCollectionItemTestingDataset>: 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.
trainingDatasets List<GetModelsModelCollectionItemTrainingDataset>: The base entity which is the input for creating and training a model.
validationDatasets List<GetModelsModelCollectionItemValidationDataset>: 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: The ID of the compartment in which to list resources.
confidenceThreshold number: The intersection over the union threshold used for calculating precision and recall.
definedTags {[key: string]: string}: Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
description string: An optional description of the model.
displayName string: A filter to return only resources that match the entire display name given.
freeformTags {[key: string]: string}: 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"}
id string: The filter to find the model with the given identifier.
isQuickMode boolean: Set to true when experimenting with a new model type or dataset, so 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: What type of Vision model this is.
modelVersion string: The version of the model.
precision number: The precision of the trained model.
projectId string: The ID of the project for which to list the objects.
recall number: Recall of the trained model.
state string: The filter to match models with the given lifecycleState.
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.
testingDatasets GetModelsModelCollectionItemTestingDataset[]: 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.
trainingDatasets GetModelsModelCollectionItemTrainingDataset[]: The base entity which is the input for creating and training a model.
validationDatasets GetModelsModelCollectionItemValidationDataset[]: 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: The ID of the compartment in which to list resources.
confidence_threshold float: The intersection over the union threshold used for calculating precision and recall.
defined_tags Mapping[str, str]: Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
description str: An optional description of the model.
display_name str: A filter to return only resources that match the entire display name given.
freeform_tags Mapping[str, str]: 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"}
id str: The filter to find the model with the given identifier.
is_quick_mode bool: Set to true when experimenting with a new model type or dataset, so 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: What type of Vision model this is.
model_version str: The version of the model.
precision float: The precision of the trained model.
project_id str: The ID of the project for which to list the objects.
recall float: Recall of the trained model.
state str: The filter to match models with the given lifecycleState.
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_datasets Sequence[GetModelsModelCollectionItemTestingDataset]: 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_datasets Sequence[GetModelsModelCollectionItemTrainingDataset]: The base entity which is the input for creating and training a model.
validation_datasets Sequence[GetModelsModelCollectionItemValidationDataset]: 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: The ID of the compartment in which to list resources.
confidenceThreshold Number: The intersection over the union threshold used for calculating precision and recall.
definedTags Map<String>: Defined tags for this resource. Each key is predefined and scoped to a namespace. For example: {"foo-namespace": {"bar-key": "value"}}
description String: An optional description of the model.
displayName String: A filter to return only resources that match the entire display name given.
freeformTags Map<String>: 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"}
id String: The filter to find the model with the given identifier.
isQuickMode Boolean: Set to true when experimenting with a new model type or dataset, so 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: What type of Vision model this is.
modelVersion String: The version of the model.
precision Number: The precision of the trained model.
projectId String: The ID of the project for which to list the objects.
recall Number: Recall of the trained model.
state String: The filter to match models with the given lifecycleState.
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.
testingDatasets List<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.
trainingDatasets List<Property Map>: The base entity which is the input for creating and training a model.
validationDatasets List<Property Map>: The base entity which is the input for creating and training a model.

GetModelsModelCollectionItemTestingDataset

Bucket string: The name of the Object Storage bucket that contains the input data file.
DatasetId string: OCID of the Data Labeling dataset.
DatasetType string: The dataset type, based on where it is stored.
NamespaceName string
Object string: The object name of the input data file.

Bucket string: The name of the Object Storage bucket that contains the input data file.
DatasetId string: OCID of the Data Labeling dataset.
DatasetType string: The dataset type, based on where it is stored.
NamespaceName string
Object string: The object name of the input data file.

bucket String: The name of the Object Storage bucket that contains the input data file.
datasetId String: OCID of the Data Labeling dataset.
datasetType String: The dataset type, based on where it is stored.
namespaceName String
object String: The object name of the input data file.

bucket string: The name of the Object Storage bucket that contains the input data file.
datasetId string: OCID of the Data Labeling dataset.
datasetType string: The dataset type, based on where it is stored.
namespaceName string
object string: The object name of the input data file.

bucket str: The name of the Object Storage bucket that contains the input data file.
dataset_id str: OCID of the Data Labeling dataset.
dataset_type str: The dataset type, based on where it is stored.
namespace_name str
object str: The object name of the input data file.

bucket String: The name of the Object Storage bucket that contains the input data file.
datasetId String: OCID of the Data Labeling dataset.
datasetType String: The dataset type, based on where it is stored.
namespaceName String
object String: The object name of the input data file.

GetModelsModelCollectionItemTrainingDataset

Bucket string: The name of the Object Storage bucket that contains the input data file.
DatasetId string: OCID of the Data Labeling dataset.
DatasetType string: The dataset type, based on where it is stored.
NamespaceName string
Object string: The object name of the input data file.

Bucket string: The name of the Object Storage bucket that contains the input data file.
DatasetId string: OCID of the Data Labeling dataset.
DatasetType string: The dataset type, based on where it is stored.
NamespaceName string
Object string: The object name of the input data file.

bucket String: The name of the Object Storage bucket that contains the input data file.
datasetId String: OCID of the Data Labeling dataset.
datasetType String: The dataset type, based on where it is stored.
namespaceName String
object String: The object name of the input data file.

bucket string: The name of the Object Storage bucket that contains the input data file.
datasetId string: OCID of the Data Labeling dataset.
datasetType string: The dataset type, based on where it is stored.
namespaceName string
object string: The object name of the input data file.

bucket str: The name of the Object Storage bucket that contains the input data file.
dataset_id str: OCID of the Data Labeling dataset.
dataset_type str: The dataset type, based on where it is stored.
namespace_name str
object str: The object name of the input data file.

bucket String: The name of the Object Storage bucket that contains the input data file.
datasetId String: OCID of the Data Labeling dataset.
datasetType String: The dataset type, based on where it is stored.
namespaceName String
object String: The object name of the input data file.

GetModelsModelCollectionItemValidationDataset

Bucket string: The name of the Object Storage bucket that contains the input data file.
DatasetId string: OCID of the Data Labeling dataset.
DatasetType string: The dataset type, based on where it is stored.
NamespaceName string
Object string: The object name of the input data file.

Bucket string: The name of the Object Storage bucket that contains the input data file.
DatasetId string: OCID of the Data Labeling dataset.
DatasetType string: The dataset type, based on where it is stored.
NamespaceName string
Object string: The object name of the input data file.

bucket String: The name of the Object Storage bucket that contains the input data file.
datasetId String: OCID of the Data Labeling dataset.
datasetType String: The dataset type, based on where it is stored.
namespaceName String
object String: The object name of the input data file.

bucket string: The name of the Object Storage bucket that contains the input data file.
datasetId string: OCID of the Data Labeling dataset.
datasetType string: The dataset type, based on where it is stored.
namespaceName string
object string: The object name of the input data file.

bucket str: The name of the Object Storage bucket that contains the input data file.
dataset_id str: OCID of the Data Labeling dataset.
dataset_type str: The dataset type, based on where it is stored.
namespace_name str
object str: The object name of the input data file.

bucket String: The name of the Object Storage bucket that contains the input data file.
datasetId String: OCID of the Data Labeling dataset.
datasetType String: The dataset type, based on where it is stored.
namespaceName String
object String: The object name of the input data file.

Package Details

Repository: oci pulumi/pulumi-oci
License: Apache-2.0
Notes: This Pulumi package is based on the oci Terraform Provider.

Oracle Cloud Infrastructure v3.9.0 published on Wednesday, Sep 24, 2025 by Pulumi

pulumi/pulumi-oci

oci.AiVision.getModels

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Example Usage

Using getModels

getModels Result

Supporting Types

GetModelsFilter

GetModelsModelCollection

GetModelsModelCollectionItem

GetModelsModelCollectionItemTestingDataset

GetModelsModelCollectionItemTrainingDataset

GetModelsModelCollectionItemValidationDataset

Package Details

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