Oracle Cloud Infrastructure v2.17.0, Nov 15 24

Oracle Cloud Infrastructure v2.17.0 published on Friday, Nov 15, 2024 by Pulumi

oci.AiVision.getModels

Explore with Pulumi AI

Oracle Cloud Infrastructure v2.17.0 published on Friday, Nov 15, 2024 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/v2/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[_aivision.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[_aivision.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)
// Output-based functions aren't available in Java yet

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: unique Model identifier
ProjectId string: The ID of the project for which to list the objects.
State string: A filter to return only resources their lifecycleState matches 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: unique Model identifier
ProjectId string: The ID of the project for which to list the objects.
State string: A filter to return only resources their lifecycleState matches 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: unique Model identifier
projectId String: The ID of the project for which to list the objects.
state String: A filter to return only resources their lifecycleState matches 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: unique Model identifier
projectId string: The ID of the project for which to list the objects.
state string: A filter to return only resources their lifecycleState matches 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[aivision.GetModelsFilter]
id str: unique Model identifier
project_id str: The ID of the project for which to list the objects.
state str: A filter to return only resources their lifecycleState matches 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: unique Model identifier
projectId String: The ID of the project for which to list the objects.
state String: A filter to return only resources their lifecycleState matches the given lifecycleState.

getModels Result

The following output properties are available:

ModelCollections List<GetModelsModelCollection>: The list of model_collection.
CompartmentId string: Compartment Identifier
DisplayName string: Model Identifier, can be renamed
Filters List<GetModelsFilter>
Id string: Unique identifier that is immutable on creation
ProjectId string: The OCID of the project to associate with the model.
State string: The current state of the Model.

ModelCollections []GetModelsModelCollection: The list of model_collection.
CompartmentId string: Compartment Identifier
DisplayName string: Model Identifier, can be renamed
Filters []GetModelsFilter
Id string: Unique identifier that is immutable on creation
ProjectId string: The OCID of the project to associate with the model.
State string: The current state of the Model.

modelCollections List<GetModelsModelCollection>: The list of model_collection.
compartmentId String: Compartment Identifier
displayName String: Model Identifier, can be renamed
filters List<GetModelsFilter>
id String: Unique identifier that is immutable on creation
projectId String: The OCID of the project to associate with the model.
state String: The current state of the Model.

modelCollections GetModelsModelCollection[]: The list of model_collection.
compartmentId string: Compartment Identifier
displayName string: Model Identifier, can be renamed
filters GetModelsFilter[]
id string: Unique identifier that is immutable on creation
projectId string: The OCID of the project to associate with the model.
state string: The current state of the Model.

model_collections Sequence[aivision.GetModelsModelCollection]: The list of model_collection.
compartment_id str: Compartment Identifier
display_name str: Model Identifier, can be renamed
filters Sequence[aivision.GetModelsFilter]
id str: Unique identifier that is immutable on creation
project_id str: The OCID of the project to associate with the model.
state str: The current state of the Model.

modelCollections List<Property Map>: The list of model_collection.
compartmentId String: Compartment Identifier
displayName String: Model Identifier, can be renamed
filters List<Property Map>
id String: Unique identifier that is immutable on creation
projectId String: The OCID of the project to associate with 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[aivision.GetModelsModelCollectionItem]

items List<Property Map>

GetModelsModelCollectionItem

AveragePrecision double: Average precision of the trained model
CompartmentId string: The ID of the compartment in which to list resources.
ConfidenceThreshold double: Confidence ratio of the calculation
DefinedTags Dictionary<string, string>: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
Description string: A short description of the model.
DisplayName string: A filter to return only resources that match the entire display name given.
FreeformTags Dictionary<string, string>: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
Id string: unique Model identifier
IsQuickMode bool: If It's true, Training is set for recommended epochs needed for quick training.
LifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
MaxTrainingDurationInHours double: The maximum duration in hours for which the training will run.
Metrics string: Complete Training Metrics for successful trained model
ModelType string: Type of the Model.
ModelVersion string: The version of the model
Precision double: 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: A filter to return only resources their lifecycleState matches the given lifecycleState.
SystemTags Dictionary<string, string>: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
TestImageCount int: Total number of testing Images
TestingDatasets List<GetModelsModelCollectionItemTestingDataset>: The base entity for a Dataset, which is the input for Model creation.
TimeCreated string: The time the Model was created. An RFC3339 formatted datetime string
TimeUpdated string: The time the Model was updated. An RFC3339 formatted datetime string
TotalImageCount int: Total number of training Images
TrainedDurationInHours double: Total hours actually used for training
TrainingDatasets List<GetModelsModelCollectionItemTrainingDataset>: The base entity for a Dataset, which is the input for Model creation.
ValidationDatasets List<GetModelsModelCollectionItemValidationDataset>: The base entity for a Dataset, which is the input for Model creation.

AveragePrecision float64: Average precision of the trained model
CompartmentId string: The ID of the compartment in which to list resources.
ConfidenceThreshold float64: Confidence ratio of the calculation
DefinedTags map[string]string: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
Description string: A short description of the model.
DisplayName string: A filter to return only resources that match the entire display name given.
FreeformTags map[string]string: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
Id string: unique Model identifier
IsQuickMode bool: If It's true, Training is set for recommended epochs needed for quick training.
LifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
MaxTrainingDurationInHours float64: The maximum duration in hours for which the training will run.
Metrics string: Complete Training Metrics for successful trained model
ModelType string: Type of the Model.
ModelVersion string: The version of the model
Precision float64: 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: A filter to return only resources their lifecycleState matches the given lifecycleState.
SystemTags map[string]string: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
TestImageCount int: Total number of testing Images
TestingDatasets []GetModelsModelCollectionItemTestingDataset: The base entity for a Dataset, which is the input for Model creation.
TimeCreated string: The time the Model was created. An RFC3339 formatted datetime string
TimeUpdated string: The time the Model was updated. An RFC3339 formatted datetime string
TotalImageCount int: Total number of training Images
TrainedDurationInHours float64: Total hours actually used for training
TrainingDatasets []GetModelsModelCollectionItemTrainingDataset: The base entity for a Dataset, which is the input for Model creation.
ValidationDatasets []GetModelsModelCollectionItemValidationDataset: The base entity for a Dataset, which is the input for Model creation.

averagePrecision Double: Average precision of the trained model
compartmentId String: The ID of the compartment in which to list resources.
confidenceThreshold Double: Confidence ratio of the calculation
definedTags Map<String,String>: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description String: A short description of the model.
displayName String: A filter to return only resources that match the entire display name given.
freeformTags Map<String,String>: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id String: unique Model identifier
isQuickMode Boolean: If It's true, Training is set for recommended epochs needed for quick training.
lifecycleDetails String: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
maxTrainingDurationInHours Double: The maximum duration in hours for which the training will run.
metrics String: Complete Training Metrics for successful trained model
modelType String: Type of the Model.
modelVersion String: The version of the model
precision Double: 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: A filter to return only resources their lifecycleState matches the given lifecycleState.
systemTags Map<String,String>: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
testImageCount Integer: Total number of testing Images
testingDatasets List<GetModelsModelCollectionItemTestingDataset>: The base entity for a Dataset, which is the input for Model creation.
timeCreated String: The time the Model was created. An RFC3339 formatted datetime string
timeUpdated String: The time the Model was updated. An RFC3339 formatted datetime string
totalImageCount Integer: Total number of training Images
trainedDurationInHours Double: Total hours actually used for training
trainingDatasets List<GetModelsModelCollectionItemTrainingDataset>: The base entity for a Dataset, which is the input for Model creation.
validationDatasets List<GetModelsModelCollectionItemValidationDataset>: The base entity for a Dataset, which is the input for Model creation.

averagePrecision number: Average precision of the trained model
compartmentId string: The ID of the compartment in which to list resources.
confidenceThreshold number: Confidence ratio of the calculation
definedTags {[key: string]: string}: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description string: A short description of the model.
displayName string: A filter to return only resources that match the entire display name given.
freeformTags {[key: string]: string}: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id string: unique Model identifier
isQuickMode boolean: If It's true, Training is set for recommended epochs needed for quick training.
lifecycleDetails string: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
maxTrainingDurationInHours number: The maximum duration in hours for which the training will run.
metrics string: Complete Training Metrics for successful trained model
modelType string: Type of the Model.
modelVersion string: The version of the model
precision number: 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: A filter to return only resources their lifecycleState matches the given lifecycleState.
systemTags {[key: string]: string}: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
testImageCount number: Total number of testing Images
testingDatasets GetModelsModelCollectionItemTestingDataset[]: The base entity for a Dataset, which is the input for Model creation.
timeCreated string: The time the Model was created. An RFC3339 formatted datetime string
timeUpdated string: The time the Model was updated. An RFC3339 formatted datetime string
totalImageCount number: Total number of training Images
trainedDurationInHours number: Total hours actually used for training
trainingDatasets GetModelsModelCollectionItemTrainingDataset[]: The base entity for a Dataset, which is the input for Model creation.
validationDatasets GetModelsModelCollectionItemValidationDataset[]: The base entity for a Dataset, which is the input for Model creation.

average_precision float: Average precision of the trained model
compartment_id str: The ID of the compartment in which to list resources.
confidence_threshold float: Confidence ratio of the calculation
defined_tags Mapping[str, str]: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description str: A short 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]: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id str: unique Model identifier
is_quick_mode bool: If It's true, Training is set for recommended epochs needed for quick training.
lifecycle_details str: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
max_training_duration_in_hours float: The maximum duration in hours for which the training will run.
metrics str: Complete Training Metrics for successful trained model
model_type str: Type of the Model.
model_version str: The version of the model
precision float: 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: A filter to return only resources their lifecycleState matches the given lifecycleState.
system_tags Mapping[str, str]: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
test_image_count int: Total number of testing Images
testing_datasets Sequence[aivision.GetModelsModelCollectionItemTestingDataset]: The base entity for a Dataset, which is the input for Model creation.
time_created str: The time the Model was created. An RFC3339 formatted datetime string
time_updated str: The time the Model was updated. An RFC3339 formatted datetime string
total_image_count int: Total number of training Images
trained_duration_in_hours float: Total hours actually used for training
training_datasets Sequence[aivision.GetModelsModelCollectionItemTrainingDataset]: The base entity for a Dataset, which is the input for Model creation.
validation_datasets Sequence[aivision.GetModelsModelCollectionItemValidationDataset]: The base entity for a Dataset, which is the input for Model creation.

averagePrecision Number: Average precision of the trained model
compartmentId String: The ID of the compartment in which to list resources.
confidenceThreshold Number: Confidence ratio of the calculation
definedTags Map<String>: Defined tags for this resource. Each key is predefined and scoped to a namespace. Example: {"foo-namespace.bar-key": "value"}
description String: A short description of the model.
displayName String: A filter to return only resources that match the entire display name given.
freeformTags Map<String>: Simple key-value pair that is applied without any predefined name, type or scope. Exists for cross-compatibility only. Example: {"bar-key": "value"}
id String: unique Model identifier
isQuickMode Boolean: If It's true, Training is set for recommended epochs needed for quick training.
lifecycleDetails String: A message describing the current state in more detail. For example, can be used to provide actionable information for a resource in Failed state.
maxTrainingDurationInHours Number: The maximum duration in hours for which the training will run.
metrics String: Complete Training Metrics for successful trained model
modelType String: Type of the Model.
modelVersion String: The version of the model
precision Number: 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: A filter to return only resources their lifecycleState matches the given lifecycleState.
systemTags Map<String>: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
testImageCount Number: Total number of testing Images
testingDatasets List<Property Map>: The base entity for a Dataset, which is the input for Model creation.
timeCreated String: The time the Model was created. An RFC3339 formatted datetime string
timeUpdated String: The time the Model was updated. An RFC3339 formatted datetime string
totalImageCount Number: Total number of training Images
trainedDurationInHours Number: Total hours actually used for training
trainingDatasets List<Property Map>: The base entity for a Dataset, which is the input for Model creation.
validationDatasets List<Property Map>: The base entity for a Dataset, which is the input for Model creation.

GetModelsModelCollectionItemTestingDataset

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

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

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

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

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

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

GetModelsModelCollectionItemTrainingDataset

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

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

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

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

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

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

GetModelsModelCollectionItemValidationDataset

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

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

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

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

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

bucket String: The name of the ObjectStorage bucket that contains the input data file.
datasetId String: The OCID of the Data Science Labeling Dataset.
datasetType String: Type of the Dataset.
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 v2.17.0 published on Friday, Nov 15, 2024 by Pulumi

pulumi/pulumi-oci

oci.AiVision.getModels

On this page

On this page

Example Usage

Using getModels

getModels Result

Supporting Types

GetModelsFilter

GetModelsModelCollection

GetModelsModelCollectionItem

GetModelsModelCollectionItemTestingDataset

GetModelsModelCollectionItemTrainingDataset

GetModelsModelCollectionItemValidationDataset

Package Details

On this page

On this page