1. Packages
  2. Oracle Cloud Infrastructure
  3. API Docs
  4. GenerativeAi
  5. getModel
Oracle Cloud Infrastructure v1.36.0 published on Thursday, May 16, 2024 by Pulumi

oci.GenerativeAi.getModel

Explore with Pulumi AI

oci logo
Oracle Cloud Infrastructure v1.36.0 published on Thursday, May 16, 2024 by Pulumi

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

    Gets information about a custom model.

    Example Usage

    import * as pulumi from "@pulumi/pulumi";
    import * as oci from "@pulumi/oci";
    
    const testModel = oci.GenerativeAi.getModel({
        modelId: testModelOciGenerativeAiModel.id,
    });
    
    import pulumi
    import pulumi_oci as oci
    
    test_model = oci.GenerativeAi.get_model(model_id=test_model_oci_generative_ai_model["id"])
    
    package main
    
    import (
    	"github.com/pulumi/pulumi-oci/sdk/go/oci/GenerativeAi"
    	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
    )
    
    func main() {
    	pulumi.Run(func(ctx *pulumi.Context) error {
    		_, err := GenerativeAi.GetModel(ctx, &generativeai.GetModelArgs{
    			ModelId: testModelOciGenerativeAiModel.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.GenerativeAi.GetModel.Invoke(new()
        {
            ModelId = testModelOciGenerativeAiModel.Id,
        });
    
    });
    
    package generated_program;
    
    import com.pulumi.Context;
    import com.pulumi.Pulumi;
    import com.pulumi.core.Output;
    import com.pulumi.oci.GenerativeAi.GenerativeAiFunctions;
    import com.pulumi.oci.GenerativeAi.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 = GenerativeAiFunctions.getModel(GetModelArgs.builder()
                .modelId(testModelOciGenerativeAiModel.id())
                .build());
    
        }
    }
    
    variables:
      testModel:
        fn::invoke:
          Function: oci:GenerativeAi:getModel
          Arguments:
            modelId: ${testModelOciGenerativeAiModel.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:GenerativeAi/getModel:getModel
      arguments:
        # arguments dictionary

    The following arguments are supported:

    ModelId string
    The model OCID
    ModelId string
    The model OCID
    modelId String
    The model OCID
    modelId string
    The model OCID
    model_id str
    The model OCID
    modelId String
    The model OCID

    getModel Result

    The following output properties are available:

    BaseModelId string
    The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
    Capabilities List<string>
    Describes what this model can be used for.
    CompartmentId string
    The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    DefinedTags Dictionary<string, object>
    Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see Resource Tags. Example: {"Operations.CostCenter": "42"}
    Description string
    An optional description of the model.
    DisplayName string
    A user-friendly name.
    FineTuneDetails List<GetModelFineTuneDetail>
    Details about fine-tuning a custom model.
    FreeformTags Dictionary<string, object>
    Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example: {"Department": "Finance"}
    Id string
    An ID that uniquely identifies a pretrained or fine-tuned model.
    IsLongTermSupported bool
    Whether a model is supported long-term. Only applicable to base models.
    LifecycleDetails string
    A message describing the current state of the model in more detail that can provide actionable information.
    ModelId string
    ModelMetrics List<GetModelModelMetric>
    Model metrics during the creation of a new model.
    State string
    The lifecycle state of the model.
    SystemTags Dictionary<string, object>
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    TimeCreated string
    The date and time that the model was created in the format of an RFC3339 datetime string.
    TimeDeprecated string
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    TimeUpdated string
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    Type string
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    Vendor string
    The provider of the base model.
    Version string
    The version of the model.
    BaseModelId string
    The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
    Capabilities []string
    Describes what this model can be used for.
    CompartmentId string
    The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    DefinedTags map[string]interface{}
    Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see Resource Tags. Example: {"Operations.CostCenter": "42"}
    Description string
    An optional description of the model.
    DisplayName string
    A user-friendly name.
    FineTuneDetails []GetModelFineTuneDetail
    Details about fine-tuning a custom model.
    FreeformTags map[string]interface{}
    Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example: {"Department": "Finance"}
    Id string
    An ID that uniquely identifies a pretrained or fine-tuned model.
    IsLongTermSupported bool
    Whether a model is supported long-term. Only applicable to base models.
    LifecycleDetails string
    A message describing the current state of the model in more detail that can provide actionable information.
    ModelId string
    ModelMetrics []GetModelModelMetric
    Model metrics during the creation of a new model.
    State string
    The lifecycle state of the model.
    SystemTags map[string]interface{}
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    TimeCreated string
    The date and time that the model was created in the format of an RFC3339 datetime string.
    TimeDeprecated string
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    TimeUpdated string
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    Type string
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    Vendor string
    The provider of the base model.
    Version string
    The version of the model.
    baseModelId String
    The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
    capabilities List<String>
    Describes what this model can be used for.
    compartmentId String
    The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    definedTags Map<String,Object>
    Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see Resource Tags. Example: {"Operations.CostCenter": "42"}
    description String
    An optional description of the model.
    displayName String
    A user-friendly name.
    fineTuneDetails List<GetModelFineTuneDetail>
    Details about fine-tuning a custom model.
    freeformTags Map<String,Object>
    Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example: {"Department": "Finance"}
    id String
    An ID that uniquely identifies a pretrained or fine-tuned model.
    isLongTermSupported Boolean
    Whether a model is supported long-term. Only applicable to base models.
    lifecycleDetails String
    A message describing the current state of the model in more detail that can provide actionable information.
    modelId String
    modelMetrics List<GetModelModelMetric>
    Model metrics during the creation of a new model.
    state String
    The lifecycle state of the model.
    systemTags Map<String,Object>
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated String
    The date and time that the model was created in the format of an RFC3339 datetime string.
    timeDeprecated String
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    timeUpdated String
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    type String
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    vendor String
    The provider of the base model.
    version String
    The version of the model.
    baseModelId string
    The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
    capabilities string[]
    Describes what this model can be used for.
    compartmentId string
    The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    definedTags {[key: string]: any}
    Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see Resource Tags. Example: {"Operations.CostCenter": "42"}
    description string
    An optional description of the model.
    displayName string
    A user-friendly name.
    fineTuneDetails GetModelFineTuneDetail[]
    Details about fine-tuning a custom model.
    freeformTags {[key: string]: any}
    Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example: {"Department": "Finance"}
    id string
    An ID that uniquely identifies a pretrained or fine-tuned model.
    isLongTermSupported boolean
    Whether a model is supported long-term. Only applicable to base models.
    lifecycleDetails string
    A message describing the current state of the model in more detail that can provide actionable information.
    modelId string
    modelMetrics GetModelModelMetric[]
    Model metrics during the creation of a new model.
    state string
    The lifecycle state of the model.
    systemTags {[key: string]: any}
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated string
    The date and time that the model was created in the format of an RFC3339 datetime string.
    timeDeprecated string
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    timeUpdated string
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    type string
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    vendor string
    The provider of the base model.
    version string
    The version of the model.
    base_model_id str
    The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
    capabilities Sequence[str]
    Describes what this model can be used for.
    compartment_id str
    The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    defined_tags Mapping[str, Any]
    Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see Resource Tags. Example: {"Operations.CostCenter": "42"}
    description str
    An optional description of the model.
    display_name str
    A user-friendly name.
    fine_tune_details Sequence[generativeai.GetModelFineTuneDetail]
    Details about fine-tuning a custom model.
    freeform_tags Mapping[str, Any]
    Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example: {"Department": "Finance"}
    id str
    An ID that uniquely identifies a pretrained or fine-tuned model.
    is_long_term_supported bool
    Whether a model is supported long-term. Only applicable to base models.
    lifecycle_details str
    A message describing the current state of the model in more detail that can provide actionable information.
    model_id str
    model_metrics Sequence[generativeai.GetModelModelMetric]
    Model metrics during the creation of a new model.
    state str
    The lifecycle state of the model.
    system_tags Mapping[str, Any]
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    time_created str
    The date and time that the model was created in the format of an RFC3339 datetime string.
    time_deprecated str
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    time_updated str
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    type str
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    vendor str
    The provider of the base model.
    version str
    The version of the model.
    baseModelId String
    The OCID of the base model that's used for fine-tuning. For pretrained models, the value is null.
    capabilities List<String>
    Describes what this model can be used for.
    compartmentId String
    The compartment OCID for fine-tuned models. For pretrained models, this value is null.
    definedTags Map<Any>
    Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see Resource Tags. Example: {"Operations.CostCenter": "42"}
    description String
    An optional description of the model.
    displayName String
    A user-friendly name.
    fineTuneDetails List<Property Map>
    Details about fine-tuning a custom model.
    freeformTags Map<Any>
    Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see Resource Tags. Example: {"Department": "Finance"}
    id String
    An ID that uniquely identifies a pretrained or fine-tuned model.
    isLongTermSupported Boolean
    Whether a model is supported long-term. Only applicable to base models.
    lifecycleDetails String
    A message describing the current state of the model in more detail that can provide actionable information.
    modelId String
    modelMetrics List<Property Map>
    Model metrics during the creation of a new model.
    state String
    The lifecycle state of the model.
    systemTags Map<Any>
    System tags for this resource. Each key is predefined and scoped to a namespace. Example: {"orcl-cloud.free-tier-retained": "true"}
    timeCreated String
    The date and time that the model was created in the format of an RFC3339 datetime string.
    timeDeprecated String
    Corresponds to the time when the custom model and its associated foundation model will be deprecated.
    timeUpdated String
    The date and time that the model was updated in the format of an RFC3339 datetime string.
    type String
    The model type indicating whether this is a pretrained/base model or a custom/fine-tuned model.
    vendor String
    The provider of the base model.
    version String
    The version of the model.

    Supporting Types

    GetModelFineTuneDetail

    DedicatedAiClusterId string
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    TrainingConfigs List<GetModelFineTuneDetailTrainingConfig>
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    TrainingDatasets List<GetModelFineTuneDetailTrainingDataset>
    The dataset used to fine-tune the model.
    DedicatedAiClusterId string
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    TrainingConfigs []GetModelFineTuneDetailTrainingConfig
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    TrainingDatasets []GetModelFineTuneDetailTrainingDataset
    The dataset used to fine-tune the model.
    dedicatedAiClusterId String
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    trainingConfigs List<GetModelFineTuneDetailTrainingConfig>
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    trainingDatasets List<GetModelFineTuneDetailTrainingDataset>
    The dataset used to fine-tune the model.
    dedicatedAiClusterId string
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    trainingConfigs GetModelFineTuneDetailTrainingConfig[]
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    trainingDatasets GetModelFineTuneDetailTrainingDataset[]
    The dataset used to fine-tune the model.
    dedicated_ai_cluster_id str
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    training_configs Sequence[generativeai.GetModelFineTuneDetailTrainingConfig]
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    training_datasets Sequence[generativeai.GetModelFineTuneDetailTrainingDataset]
    The dataset used to fine-tune the model.
    dedicatedAiClusterId String
    The OCID of the dedicated AI cluster this fine-tuning runs on.
    trainingConfigs List<Property Map>
    The fine-tuning method and hyperparameters used for fine-tuning a custom model.
    trainingDatasets List<Property Map>
    The dataset used to fine-tune the model.

    GetModelFineTuneDetailTrainingConfig

    EarlyStoppingPatience int
    Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
    EarlyStoppingThreshold double
    How much the loss must improve to prevent early stopping.
    LearningRate double
    The initial learning rate to be used during training
    LogModelMetricsIntervalInSteps int
    Determines how frequently to log model metrics.
    NumOfLastLayers int
    The number of last layers to be fine-tuned.
    TotalTrainingEpochs int
    The maximum number of training epochs to run for.
    TrainingBatchSize int
    The batch size used during training.
    TrainingConfigType string
    The fine-tuning method for training a custom model.
    EarlyStoppingPatience int
    Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
    EarlyStoppingThreshold float64
    How much the loss must improve to prevent early stopping.
    LearningRate float64
    The initial learning rate to be used during training
    LogModelMetricsIntervalInSteps int
    Determines how frequently to log model metrics.
    NumOfLastLayers int
    The number of last layers to be fine-tuned.
    TotalTrainingEpochs int
    The maximum number of training epochs to run for.
    TrainingBatchSize int
    The batch size used during training.
    TrainingConfigType string
    The fine-tuning method for training a custom model.
    earlyStoppingPatience Integer
    Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
    earlyStoppingThreshold Double
    How much the loss must improve to prevent early stopping.
    learningRate Double
    The initial learning rate to be used during training
    logModelMetricsIntervalInSteps Integer
    Determines how frequently to log model metrics.
    numOfLastLayers Integer
    The number of last layers to be fine-tuned.
    totalTrainingEpochs Integer
    The maximum number of training epochs to run for.
    trainingBatchSize Integer
    The batch size used during training.
    trainingConfigType String
    The fine-tuning method for training a custom model.
    earlyStoppingPatience number
    Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
    earlyStoppingThreshold number
    How much the loss must improve to prevent early stopping.
    learningRate number
    The initial learning rate to be used during training
    logModelMetricsIntervalInSteps number
    Determines how frequently to log model metrics.
    numOfLastLayers number
    The number of last layers to be fine-tuned.
    totalTrainingEpochs number
    The maximum number of training epochs to run for.
    trainingBatchSize number
    The batch size used during training.
    trainingConfigType string
    The fine-tuning method for training a custom model.
    early_stopping_patience int
    Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
    early_stopping_threshold float
    How much the loss must improve to prevent early stopping.
    learning_rate float
    The initial learning rate to be used during training
    log_model_metrics_interval_in_steps int
    Determines how frequently to log model metrics.
    num_of_last_layers int
    The number of last layers to be fine-tuned.
    total_training_epochs int
    The maximum number of training epochs to run for.
    training_batch_size int
    The batch size used during training.
    training_config_type str
    The fine-tuning method for training a custom model.
    earlyStoppingPatience Number
    Stop training if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluation.
    earlyStoppingThreshold Number
    How much the loss must improve to prevent early stopping.
    learningRate Number
    The initial learning rate to be used during training
    logModelMetricsIntervalInSteps Number
    Determines how frequently to log model metrics.
    numOfLastLayers Number
    The number of last layers to be fine-tuned.
    totalTrainingEpochs Number
    The maximum number of training epochs to run for.
    trainingBatchSize Number
    The batch size used during training.
    trainingConfigType String
    The fine-tuning method for training a custom model.

    GetModelFineTuneDetailTrainingDataset

    Bucket string
    The Object Storage bucket name.
    DatasetType string
    The type of the data asset.
    Namespace string
    The Object Storage namespace.
    Object string
    The Object Storage object name.
    Bucket string
    The Object Storage bucket name.
    DatasetType string
    The type of the data asset.
    Namespace string
    The Object Storage namespace.
    Object string
    The Object Storage object name.
    bucket String
    The Object Storage bucket name.
    datasetType String
    The type of the data asset.
    namespace String
    The Object Storage namespace.
    object String
    The Object Storage object name.
    bucket string
    The Object Storage bucket name.
    datasetType string
    The type of the data asset.
    namespace string
    The Object Storage namespace.
    object string
    The Object Storage object name.
    bucket str
    The Object Storage bucket name.
    dataset_type str
    The type of the data asset.
    namespace str
    The Object Storage namespace.
    object str
    The Object Storage object name.
    bucket String
    The Object Storage bucket name.
    datasetType String
    The type of the data asset.
    namespace String
    The Object Storage namespace.
    object String
    The Object Storage object name.

    GetModelModelMetric

    FinalAccuracy double
    Fine-tuned model accuracy.
    FinalLoss double
    Fine-tuned model loss.
    ModelMetricsType string
    The type of the model metrics. Each type of model can expect a different set of model metrics.
    FinalAccuracy float64
    Fine-tuned model accuracy.
    FinalLoss float64
    Fine-tuned model loss.
    ModelMetricsType string
    The type of the model metrics. Each type of model can expect a different set of model metrics.
    finalAccuracy Double
    Fine-tuned model accuracy.
    finalLoss Double
    Fine-tuned model loss.
    modelMetricsType String
    The type of the model metrics. Each type of model can expect a different set of model metrics.
    finalAccuracy number
    Fine-tuned model accuracy.
    finalLoss number
    Fine-tuned model loss.
    modelMetricsType string
    The type of the model metrics. Each type of model can expect a different set of model metrics.
    final_accuracy float
    Fine-tuned model accuracy.
    final_loss float
    Fine-tuned model loss.
    model_metrics_type str
    The type of the model metrics. Each type of model can expect a different set of model metrics.
    finalAccuracy Number
    Fine-tuned model accuracy.
    finalLoss Number
    Fine-tuned model loss.
    modelMetricsType String
    The type of the model metrics. Each type of model can expect a different set of model metrics.

    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 v1.36.0 published on Thursday, May 16, 2024 by Pulumi