Oracle Cloud Infrastructure v3.7.0, Sep 13 25

Oracle Cloud Infrastructure v3.7.0 published on Saturday, Sep 13, 2025 by Pulumi

oci.AiVision.getModels

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Oracle Cloud Infrastructure v3.7.0 published on Saturday, Sep 13, 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 v3.7.0 published on Saturday, Sep 13, 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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