Oracle Cloud Infrastructure v1.11.0, Sep 27 23

Oracle Cloud Infrastructure v1.11.0 published on Wednesday, Sep 27, 2023 by Pulumi

oci.AiVision.getModel

Explore with Pulumi AI

Oracle Cloud Infrastructure v1.11.0 published on Wednesday, Sep 27, 2023 by Pulumi

Example Usage

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 = oci_ai_vision_model.Test_model.Id,
    });

});

package main

import (
	"github.com/pulumi/pulumi-oci/sdk/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: oci_ai_vision_model.Test_model.Id,
		}, nil)
		if err != nil {
			return err
		}
		return nil
	})
}

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(oci_ai_vision_model.test_model().id())
            .build());

    }
}

import pulumi
import pulumi_oci as oci

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

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

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

variables:
  testModel:
    fn::invoke:
      Function: oci:AiVision:getModel
      Arguments:
        modelId: ${oci_ai_vision_model.test_model.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.

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)
// Output-based functions aren't available in Java yet

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, object>: 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, object>: 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, object>: 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]interface{}: 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]interface{}: 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]interface{}: 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,Object>: 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,Object>: 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,Object>: 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]: any}: 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]: any}: 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]: any}: 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, Any]: 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, Any]: 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, Any]: 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 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 GetModelTrainingDataset]: The base entity for a Dataset, which is the input for Model creation.
validation_datasets 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<Any>: 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<Any>: 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<Any>: 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 v1.11.0 published on Wednesday, Sep 27, 2023 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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