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Oracle Cloud Infrastructure v2.2.0 published on Friday, Jul 12, 2024 by Pulumi

oci.AiVision.getModel

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Oracle Cloud Infrastructure v2.2.0 published on Friday, Jul 12, 2024 by Pulumi

    This data source provides details about a specific Model resource in Oracle Cloud Infrastructure Ai Vision service.

    Gets a Model by identifier

    Example Usage

    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.

    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 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<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.
    oci logo
    Oracle Cloud Infrastructure v2.2.0 published on Friday, Jul 12, 2024 by Pulumi