Oracle Cloud Infrastructure v1.10.0, Sep 7 23

Oracle Cloud Infrastructure v1.10.0 published on Thursday, Sep 7, 2023 by Pulumi

oci.AiVision.getModels

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Oracle Cloud Infrastructure v1.10.0 published on Thursday, Sep 7, 2023 by Pulumi

Example Usage

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 = @var.Compartment_id,
        DisplayName = @var.Model_display_name,
        Id = @var.Model_id,
        ProjectId = oci_ai_vision_project.Test_project.Id,
        State = @var.Model_state,
    });

});

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.GetModels(ctx, &aivision.GetModelsArgs{
			CompartmentId: pulumi.StringRef(_var.Compartment_id),
			DisplayName:   pulumi.StringRef(_var.Model_display_name),
			Id:            pulumi.StringRef(_var.Model_id),
			ProjectId:     pulumi.StringRef(oci_ai_vision_project.Test_project.Id),
			State:         pulumi.StringRef(_var.Model_state),
		}, 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.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(var_.compartment_id())
            .displayName(var_.model_display_name())
            .id(var_.model_id())
            .projectId(oci_ai_vision_project.test_project().id())
            .state(var_.model_state())
            .build());

    }
}

import pulumi
import pulumi_oci as oci

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

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

const testModels = oci.AiVision.getModels({
    compartmentId: _var.compartment_id,
    displayName: _var.model_display_name,
    id: _var.model_id,
    projectId: oci_ai_vision_project.test_project.id,
    state: _var.model_state,
});

variables:
  testModels:
    fn::invoke:
      Function: oci:AiVision:getModels
      Arguments:
        compartmentId: ${var.compartment_id}
        displayName: ${var.model_display_name}
        id: ${var.model_id}
        projectId: ${oci_ai_vision_project.test_project.id}
        state: ${var.model_state}

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 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 GetModelsModelCollection]: The list of model_collection.
compartment_id str: Compartment Identifier
display_name str: Model Identifier, can be renamed
filters 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 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, 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: A filter to return only resources that match the entire display name given.
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 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, 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<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]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: A filter to return only resources that match the entire display name given.
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 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]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 []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,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: A filter to return only resources that match the entire display name given.
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 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,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<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]: 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: A filter to return only resources that match the entire display name given.
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 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]: 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 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, 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: A filter to return only resources that match the entire display name given.
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 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, 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 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 GetModelsModelCollectionItemTrainingDataset]: The base entity for a Dataset, which is the input for Model creation.
validation_datasets 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<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: A filter to return only resources that match the entire display name given.
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 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<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.

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 v1.10.0 published on Thursday, Sep 7, 2023 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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