Oracle Cloud Infrastructure v2.28.0, Mar 27 25

Oracle Cloud Infrastructure v2.28.0 published on Thursday, Mar 27, 2025 by Pulumi

oci.AiVision.getModel

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Oracle Cloud Infrastructure v2.28.0 published on Thursday, Mar 27, 2025 by Pulumi

Example Usage

TypeScript
Python
Go
C#
Java
YAML

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

const testModel = oci.AiVision.getModel({
    modelId: testModelOciAiVisionModel.id,
});

import pulumi
import pulumi_oci as oci

test_model = oci.AiVision.get_model(model_id=test_model_oci_ai_vision_model["id"])

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.GetModel(ctx, &aivision.GetModelArgs{
			ModelId: testModelOciAiVisionModel.Id,
		}, 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 testModel = Oci.AiVision.GetModel.Invoke(new()
    {
        ModelId = testModelOciAiVisionModel.Id,
    });

});

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.GetModelArgs;
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 testModel = AiVisionFunctions.getModel(GetModelArgs.builder()
            .modelId(testModelOciAiVisionModel.id())
            .build());

    }
}

variables:
  testModel:
    fn::invoke:
      function: oci:AiVision:getModel
      arguments:
        modelId: ${testModelOciAiVisionModel.id}

Using getModel

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.

TypeScript
Python
Go
C#
Java
YAML

function getModel(args: GetModelArgs, opts?: InvokeOptions): Promise<GetModelResult>
function getModelOutput(args: GetModelOutputArgs, opts?: InvokeOptions): Output<GetModelResult>

def get_model(model_id: Optional[str] = None,
              opts: Optional[InvokeOptions] = None) -> GetModelResult
def get_model_output(model_id: Optional[pulumi.Input[str]] = None,
              opts: Optional[InvokeOptions] = None) -> Output[GetModelResult]

func GetModel(ctx *Context, args *GetModelArgs, opts ...InvokeOption) (*GetModelResult, error)
func GetModelOutput(ctx *Context, args *GetModelOutputArgs, opts ...InvokeOption) GetModelResultOutput

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

public static class GetModel 
{
    public static Task<GetModelResult> InvokeAsync(GetModelArgs args, InvokeOptions? opts = null)
    public static Output<GetModelResult> Invoke(GetModelInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetModelResult> getModel(GetModelArgs args, InvokeOptions options)
public static Output<GetModelResult> getModel(GetModelArgs args, InvokeOptions options)

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

The following arguments are supported:

ModelId string: unique Model identifier

ModelId string: unique Model identifier

modelId String: unique Model identifier

modelId string: unique Model identifier

model_id str: unique Model identifier

modelId String: unique Model identifier

getModel Result

The following output properties are available:

AveragePrecision double: Average precision of the trained model
CompartmentId string: Compartment Identifier
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: Model Identifier, can be renamed
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 identifier that is immutable on creation
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
ModelId string
ModelType string: Type of the Model.
ModelVersion string: The version of the model
Precision double: Precision of the trained model
ProjectId string: The OCID of the project to associate with the model.
Recall double: Recall of the trained model
State string: The current state of the Model.
SystemTags Dictionary<string, string>: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
TestImageCount int: Total number of testing Images
TestingDatasets List<GetModelTestingDataset>: 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<GetModelTrainingDataset>: The base entity for a Dataset, which is the input for Model creation.
ValidationDatasets List<GetModelValidationDataset>: The base entity for a Dataset, which is the input for Model creation.

AveragePrecision float64: Average precision of the trained model
CompartmentId string: Compartment Identifier
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: Model Identifier, can be renamed
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 identifier that is immutable on creation
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
ModelId string
ModelType string: Type of the Model.
ModelVersion string: The version of the model
Precision float64: Precision of the trained model
ProjectId string: The OCID of the project to associate with the model.
Recall float64: Recall of the trained model
State string: The current state of the Model.
SystemTags map[string]string: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
TestImageCount int: Total number of testing Images
TestingDatasets []GetModelTestingDataset: 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 []GetModelTrainingDataset: The base entity for a Dataset, which is the input for Model creation.
ValidationDatasets []GetModelValidationDataset: The base entity for a Dataset, which is the input for Model creation.

averagePrecision Double: Average precision of the trained model
compartmentId String: Compartment Identifier
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: Model Identifier, can be renamed
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 identifier that is immutable on creation
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
modelId String
modelType String: Type of the Model.
modelVersion String: The version of the model
precision Double: Precision of the trained model
projectId String: The OCID of the project to associate with the model.
recall Double: Recall of the trained model
state String: The current state of the Model.
systemTags Map<String,String>: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
testImageCount Integer: Total number of testing Images
testingDatasets List<GetModelTestingDataset>: 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<GetModelTrainingDataset>: The base entity for a Dataset, which is the input for Model creation.
validationDatasets List<GetModelValidationDataset>: The base entity for a Dataset, which is the input for Model creation.

averagePrecision number: Average precision of the trained model
compartmentId string: Compartment Identifier
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: Model Identifier, can be renamed
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 identifier that is immutable on creation
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
modelId string
modelType string: Type of the Model.
modelVersion string: The version of the model
precision number: Precision of the trained model
projectId string: The OCID of the project to associate with the model.
recall number: Recall of the trained model
state string: The current state of the Model.
systemTags {[key: string]: string}: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
testImageCount number: Total number of testing Images
testingDatasets GetModelTestingDataset[]: 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 GetModelTrainingDataset[]: The base entity for a Dataset, which is the input for Model creation.
validationDatasets GetModelValidationDataset[]: 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: Compartment Identifier
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: Model Identifier, can be renamed
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 identifier that is immutable on creation
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_id str
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 OCID of the project to associate with the model.
recall float: Recall of the trained model
state str: The current state of the Model.
system_tags Mapping[str, str]: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
test_image_count int: Total number of testing Images
testing_datasets Sequence[aivision.GetModelTestingDataset]: 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.GetModelTrainingDataset]: The base entity for a Dataset, which is the input for Model creation.
validation_datasets Sequence[aivision.GetModelValidationDataset]: The base entity for a Dataset, which is the input for Model creation.

averagePrecision Number: Average precision of the trained model
compartmentId String: Compartment Identifier
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: Model Identifier, can be renamed
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 identifier that is immutable on creation
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
modelId String
modelType String: Type of the Model.
modelVersion String: The version of the model
precision Number: Precision of the trained model
projectId String: The OCID of the project to associate with the model.
recall Number: Recall of the trained model
state String: The current state of the Model.
systemTags Map<String>: Usage of system tag keys. These predefined keys are scoped to namespaces. Example: {"orcl-cloud.free-tier-retained": "true"}
testImageCount Number: Total number of testing Images
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.

Supporting Types

GetModelTestingDataset

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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
object String: The object name of the input data file.

GetModelTrainingDataset

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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
object String: The object name of the input data file.

GetModelValidationDataset

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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
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: The namespace name of the ObjectStorage bucket that contains the input data file.
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 v2.28.0 published on Thursday, Mar 27, 2025 by Pulumi

pulumi/pulumi-oci

oci.AiVision.getModel

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Example Usage

Using getModel

getModel Result

Supporting Types

GetModelTestingDataset

GetModelTrainingDataset

GetModelValidationDataset

Package Details

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