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Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
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google-native.aiplatform/v1.DataLabelingJob

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Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
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    Creates a DataLabelingJob. Auto-naming is currently not supported for this resource.

    Create DataLabelingJob Resource

    Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

    Constructor syntax

    new DataLabelingJob(name: string, args: DataLabelingJobArgs, opts?: CustomResourceOptions);
    @overload
def DataLabelingJob(resource_name: str,
                    args: DataLabelingJobArgs,
                    opts: Optional[ResourceOptions] = None)

@overload
def DataLabelingJob(resource_name: str,
                    opts: Optional[ResourceOptions] = None,
                    inputs_schema_uri: Optional[str] = None,
                    datasets: Optional[Sequence[str]] = None,
                    display_name: Optional[str] = None,
                    inputs: Optional[Any] = None,
                    instruction_uri: Optional[str] = None,
                    labeler_count: Optional[int] = None,
                    annotation_labels: Optional[Mapping[str, str]] = None,
                    encryption_spec: Optional[GoogleCloudAiplatformV1EncryptionSpecArgs] = None,
                    active_learning_config: Optional[GoogleCloudAiplatformV1ActiveLearningConfigArgs] = None,
                    labels: Optional[Mapping[str, str]] = None,
                    location: Optional[str] = None,
                    project: Optional[str] = None,
                    specialist_pools: Optional[Sequence[str]] = None)
    func NewDataLabelingJob(ctx *Context, name string, args DataLabelingJobArgs, opts ...ResourceOption) (*DataLabelingJob, error)
    public DataLabelingJob(string name, DataLabelingJobArgs args, CustomResourceOptions? opts = null)
    public DataLabelingJob(String name, DataLabelingJobArgs args)
public DataLabelingJob(String name, DataLabelingJobArgs args, CustomResourceOptions options)

    type: google-native:aiplatform/v1:DataLabelingJob
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

    Parameters

    name string
    The unique name of the resource.
    args DataLabelingJobArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args DataLabelingJobArgs
    The arguments to resource properties.
    opts ResourceOptions
    Bag of options to control resource's behavior.
    ctx Context
    Context object for the current deployment.
    name string
    The unique name of the resource.
    args DataLabelingJobArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args DataLabelingJobArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args DataLabelingJobArgs
    The arguments to resource properties.
    options CustomResourceOptions
    Bag of options to control resource's behavior.

    Constructor example

    The following reference example uses placeholder values for all input properties.

    var dataLabelingJobResource = new GoogleNative.Aiplatform.V1.DataLabelingJob("dataLabelingJobResource", new()
{
    InputsSchemaUri = "string",
    Datasets = new[]
    {
        "string",
    },
    DisplayName = "string",
    Inputs = "any",
    InstructionUri = "string",
    LabelerCount = 0,
    AnnotationLabels = 
    {
        { "string", "string" },
    },
    EncryptionSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EncryptionSpecArgs
    {
        KmsKeyName = "string",
    },
    ActiveLearningConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ActiveLearningConfigArgs
    {
        MaxDataItemCount = "string",
        MaxDataItemPercentage = 0,
        SampleConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SampleConfigArgs
        {
            FollowingBatchSamplePercentage = 0,
            InitialBatchSamplePercentage = 0,
            SampleStrategy = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1SampleConfigSampleStrategy.SampleStrategyUnspecified,
        },
        TrainingConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1TrainingConfigArgs
        {
            TimeoutTrainingMilliHours = "string",
        },
    },
    Labels = 
    {
        { "string", "string" },
    },
    Location = "string",
    Project = "string",
    SpecialistPools = new[]
    {
        "string",
    },
});

    example, err := aiplatform.NewDataLabelingJob(ctx, "dataLabelingJobResource", &aiplatform.DataLabelingJobArgs{
	InputsSchemaUri: pulumi.String("string"),
	Datasets: pulumi.StringArray{
		pulumi.String("string"),
	},
	DisplayName:    pulumi.String("string"),
	Inputs:         pulumi.Any("any"),
	InstructionUri: pulumi.String("string"),
	LabelerCount:   pulumi.Int(0),
	AnnotationLabels: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	EncryptionSpec: &aiplatform.GoogleCloudAiplatformV1EncryptionSpecArgs{
		KmsKeyName: pulumi.String("string"),
	},
	ActiveLearningConfig: &aiplatform.GoogleCloudAiplatformV1ActiveLearningConfigArgs{
		MaxDataItemCount:      pulumi.String("string"),
		MaxDataItemPercentage: pulumi.Int(0),
		SampleConfig: &aiplatform.GoogleCloudAiplatformV1SampleConfigArgs{
			FollowingBatchSamplePercentage: pulumi.Int(0),
			InitialBatchSamplePercentage:   pulumi.Int(0),
			SampleStrategy:                 aiplatform.GoogleCloudAiplatformV1SampleConfigSampleStrategySampleStrategyUnspecified,
		},
		TrainingConfig: &aiplatform.GoogleCloudAiplatformV1TrainingConfigArgs{
			TimeoutTrainingMilliHours: pulumi.String("string"),
		},
	},
	Labels: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	Location: pulumi.String("string"),
	Project:  pulumi.String("string"),
	SpecialistPools: pulumi.StringArray{
		pulumi.String("string"),
	},
})

    var dataLabelingJobResource = new DataLabelingJob("dataLabelingJobResource", DataLabelingJobArgs.builder()
    .inputsSchemaUri("string")
    .datasets("string")
    .displayName("string")
    .inputs("any")
    .instructionUri("string")
    .labelerCount(0)
    .annotationLabels(Map.of("string", "string"))
    .encryptionSpec(GoogleCloudAiplatformV1EncryptionSpecArgs.builder()
        .kmsKeyName("string")
        .build())
    .activeLearningConfig(GoogleCloudAiplatformV1ActiveLearningConfigArgs.builder()
        .maxDataItemCount("string")
        .maxDataItemPercentage(0)
        .sampleConfig(GoogleCloudAiplatformV1SampleConfigArgs.builder()
            .followingBatchSamplePercentage(0)
            .initialBatchSamplePercentage(0)
            .sampleStrategy("SAMPLE_STRATEGY_UNSPECIFIED")
            .build())
        .trainingConfig(GoogleCloudAiplatformV1TrainingConfigArgs.builder()
            .timeoutTrainingMilliHours("string")
            .build())
        .build())
    .labels(Map.of("string", "string"))
    .location("string")
    .project("string")
    .specialistPools("string")
    .build());

    data_labeling_job_resource = google_native.aiplatform.v1.DataLabelingJob("dataLabelingJobResource",
    inputs_schema_uri="string",
    datasets=["string"],
    display_name="string",
    inputs="any",
    instruction_uri="string",
    labeler_count=0,
    annotation_labels={
        "string": "string",
    },
    encryption_spec={
        "kms_key_name": "string",
    },
    active_learning_config={
        "max_data_item_count": "string",
        "max_data_item_percentage": 0,
        "sample_config": {
            "following_batch_sample_percentage": 0,
            "initial_batch_sample_percentage": 0,
            "sample_strategy": google_native.aiplatform.v1.GoogleCloudAiplatformV1SampleConfigSampleStrategy.SAMPLE_STRATEGY_UNSPECIFIED,
        },
        "training_config": {
            "timeout_training_milli_hours": "string",
        },
    },
    labels={
        "string": "string",
    },
    location="string",
    project="string",
    specialist_pools=["string"])

    const dataLabelingJobResource = new google_native.aiplatform.v1.DataLabelingJob("dataLabelingJobResource", {
    inputsSchemaUri: "string",
    datasets: ["string"],
    displayName: "string",
    inputs: "any",
    instructionUri: "string",
    labelerCount: 0,
    annotationLabels: {
        string: "string",
    },
    encryptionSpec: {
        kmsKeyName: "string",
    },
    activeLearningConfig: {
        maxDataItemCount: "string",
        maxDataItemPercentage: 0,
        sampleConfig: {
            followingBatchSamplePercentage: 0,
            initialBatchSamplePercentage: 0,
            sampleStrategy: google_native.aiplatform.v1.GoogleCloudAiplatformV1SampleConfigSampleStrategy.SampleStrategyUnspecified,
        },
        trainingConfig: {
            timeoutTrainingMilliHours: "string",
        },
    },
    labels: {
        string: "string",
    },
    location: "string",
    project: "string",
    specialistPools: ["string"],
});

    type: google-native:aiplatform/v1:DataLabelingJob
properties:
    activeLearningConfig:
        maxDataItemCount: string
        maxDataItemPercentage: 0
        sampleConfig:
            followingBatchSamplePercentage: 0
            initialBatchSamplePercentage: 0
            sampleStrategy: SAMPLE_STRATEGY_UNSPECIFIED
        trainingConfig:
            timeoutTrainingMilliHours: string
    annotationLabels:
        string: string
    datasets:
        - string
    displayName: string
    encryptionSpec:
        kmsKeyName: string
    inputs: any
    inputsSchemaUri: string
    instructionUri: string
    labelerCount: 0
    labels:
        string: string
    location: string
    project: string
    specialistPools:
        - string

    DataLabelingJob Resource Properties

    To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

    Inputs

    In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

    The DataLabelingJob resource accepts the following input properties:

    Datasets List<string>
    Dataset resource names. Right now we only support labeling from a single Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}
    DisplayName string
    The user-defined name of the DataLabelingJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a DataLabelingJob.
    Inputs object
    Input config parameters for the DataLabelingJob.
    InputsSchemaUri string
    Points to a YAML file stored on Google Cloud Storage describing the config for a specific type of DataLabelingJob. The schema files that can be used here are found in the https://storage.googleapis.com/google-cloud-aiplatform bucket in the /schema/datalabelingjob/inputs/ folder.
    InstructionUri string
    The Google Cloud Storage location of the instruction pdf. This pdf is shared with labelers, and provides detailed description on how to label DataItems in Datasets.
    LabelerCount int
    Number of labelers to work on each DataItem.
    ActiveLearningConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ActiveLearningConfig
    Parameters that configure the active learning pipeline. Active learning will label the data incrementally via several iterations. For every iteration, it will select a batch of data based on the sampling strategy.
    AnnotationLabels Dictionary<string, string>
    Labels to assign to annotations generated by this DataLabelingJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    EncryptionSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EncryptionSpec
    Customer-managed encryption key spec for a DataLabelingJob. If set, this DataLabelingJob will be secured by this key. Note: Annotations created in the DataLabelingJob are associated with the EncryptionSpec of the Dataset they are exported to.
    Labels Dictionary<string, string>
    The labels with user-defined metadata to organize your DataLabelingJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema": output only, its value is the inputs_schema's title.
    Location string
    Project string
    SpecialistPools List<string>
    The SpecialistPools' resource names associated with this job.
    Datasets []string
    Dataset resource names. Right now we only support labeling from a single Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}
    DisplayName string
    The user-defined name of the DataLabelingJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a DataLabelingJob.
    Inputs interface{}
    Input config parameters for the DataLabelingJob.
    InputsSchemaUri string
    Points to a YAML file stored on Google Cloud Storage describing the config for a specific type of DataLabelingJob. The schema files that can be used here are found in the https://storage.googleapis.com/google-cloud-aiplatform bucket in the /schema/datalabelingjob/inputs/ folder.
    InstructionUri string
    The Google Cloud Storage location of the instruction pdf. This pdf is shared with labelers, and provides detailed description on how to label DataItems in Datasets.
    LabelerCount int
    Number of labelers to work on each DataItem.
    ActiveLearningConfig GoogleCloudAiplatformV1ActiveLearningConfigArgs
    Parameters that configure the active learning pipeline. Active learning will label the data incrementally via several iterations. For every iteration, it will select a batch of data based on the sampling strategy.
    AnnotationLabels map[string]string
    Labels to assign to annotations generated by this DataLabelingJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    EncryptionSpec GoogleCloudAiplatformV1EncryptionSpecArgs
    Customer-managed encryption key spec for a DataLabelingJob. If set, this DataLabelingJob will be secured by this key. Note: Annotations created in the DataLabelingJob are associated with the EncryptionSpec of the Dataset they are exported to.
    Labels map[string]string
    The labels with user-defined metadata to organize your DataLabelingJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema": output only, its value is the inputs_schema's title.
    Location string
    Project string
    SpecialistPools []string
    The SpecialistPools' resource names associated with this job.
    datasets List<String>
    Dataset resource names. Right now we only support labeling from a single Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}
    displayName String
    The user-defined name of the DataLabelingJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a DataLabelingJob.
    inputs Object
    Input config parameters for the DataLabelingJob.
    inputsSchemaUri String
    Points to a YAML file stored on Google Cloud Storage describing the config for a specific type of DataLabelingJob. The schema files that can be used here are found in the https://storage.googleapis.com/google-cloud-aiplatform bucket in the /schema/datalabelingjob/inputs/ folder.
    instructionUri String
    The Google Cloud Storage location of the instruction pdf. This pdf is shared with labelers, and provides detailed description on how to label DataItems in Datasets.
    labelerCount Integer
    Number of labelers to work on each DataItem.
    activeLearningConfig GoogleCloudAiplatformV1ActiveLearningConfig
    Parameters that configure the active learning pipeline. Active learning will label the data incrementally via several iterations. For every iteration, it will select a batch of data based on the sampling strategy.
    annotationLabels Map<String,String>
    Labels to assign to annotations generated by this DataLabelingJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    encryptionSpec GoogleCloudAiplatformV1EncryptionSpec
    Customer-managed encryption key spec for a DataLabelingJob. If set, this DataLabelingJob will be secured by this key. Note: Annotations created in the DataLabelingJob are associated with the EncryptionSpec of the Dataset they are exported to.
    labels Map<String,String>
    The labels with user-defined metadata to organize your DataLabelingJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema": output only, its value is the inputs_schema's title.
    location String
    project String
    specialistPools List<String>
    The SpecialistPools' resource names associated with this job.
    datasets string[]
    Dataset resource names. Right now we only support labeling from a single Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}
    displayName string
    The user-defined name of the DataLabelingJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a DataLabelingJob.
    inputs any
    Input config parameters for the DataLabelingJob.
    inputsSchemaUri string
    Points to a YAML file stored on Google Cloud Storage describing the config for a specific type of DataLabelingJob. The schema files that can be used here are found in the https://storage.googleapis.com/google-cloud-aiplatform bucket in the /schema/datalabelingjob/inputs/ folder.
    instructionUri string
    The Google Cloud Storage location of the instruction pdf. This pdf is shared with labelers, and provides detailed description on how to label DataItems in Datasets.
    labelerCount number
    Number of labelers to work on each DataItem.
    activeLearningConfig GoogleCloudAiplatformV1ActiveLearningConfig
    Parameters that configure the active learning pipeline. Active learning will label the data incrementally via several iterations. For every iteration, it will select a batch of data based on the sampling strategy.
    annotationLabels {[key: string]: string}
    Labels to assign to annotations generated by this DataLabelingJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    encryptionSpec GoogleCloudAiplatformV1EncryptionSpec
    Customer-managed encryption key spec for a DataLabelingJob. If set, this DataLabelingJob will be secured by this key. Note: Annotations created in the DataLabelingJob are associated with the EncryptionSpec of the Dataset they are exported to.
    labels {[key: string]: string}
    The labels with user-defined metadata to organize your DataLabelingJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema": output only, its value is the inputs_schema's title.
    location string
    project string
    specialistPools string[]
    The SpecialistPools' resource names associated with this job.
    datasets Sequence[str]
    Dataset resource names. Right now we only support labeling from a single Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}
    display_name str
    The user-defined name of the DataLabelingJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a DataLabelingJob.
    inputs Any
    Input config parameters for the DataLabelingJob.
    inputs_schema_uri str
    Points to a YAML file stored on Google Cloud Storage describing the config for a specific type of DataLabelingJob. The schema files that can be used here are found in the https://storage.googleapis.com/google-cloud-aiplatform bucket in the /schema/datalabelingjob/inputs/ folder.
    instruction_uri str
    The Google Cloud Storage location of the instruction pdf. This pdf is shared with labelers, and provides detailed description on how to label DataItems in Datasets.
    labeler_count int
    Number of labelers to work on each DataItem.
    active_learning_config GoogleCloudAiplatformV1ActiveLearningConfigArgs
    Parameters that configure the active learning pipeline. Active learning will label the data incrementally via several iterations. For every iteration, it will select a batch of data based on the sampling strategy.
    annotation_labels Mapping[str, str]
    Labels to assign to annotations generated by this DataLabelingJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    encryption_spec GoogleCloudAiplatformV1EncryptionSpecArgs
    Customer-managed encryption key spec for a DataLabelingJob. If set, this DataLabelingJob will be secured by this key. Note: Annotations created in the DataLabelingJob are associated with the EncryptionSpec of the Dataset they are exported to.
    labels Mapping[str, str]
    The labels with user-defined metadata to organize your DataLabelingJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema": output only, its value is the inputs_schema's title.
    location str
    project str
    specialist_pools Sequence[str]
    The SpecialistPools' resource names associated with this job.
    datasets List<String>
    Dataset resource names. Right now we only support labeling from a single Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}
    displayName String
    The user-defined name of the DataLabelingJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a DataLabelingJob.
    inputs Any
    Input config parameters for the DataLabelingJob.
    inputsSchemaUri String
    Points to a YAML file stored on Google Cloud Storage describing the config for a specific type of DataLabelingJob. The schema files that can be used here are found in the https://storage.googleapis.com/google-cloud-aiplatform bucket in the /schema/datalabelingjob/inputs/ folder.
    instructionUri String
    The Google Cloud Storage location of the instruction pdf. This pdf is shared with labelers, and provides detailed description on how to label DataItems in Datasets.
    labelerCount Number
    Number of labelers to work on each DataItem.
    activeLearningConfig Property Map
    Parameters that configure the active learning pipeline. Active learning will label the data incrementally via several iterations. For every iteration, it will select a batch of data based on the sampling strategy.
    annotationLabels Map<String>
    Labels to assign to annotations generated by this DataLabelingJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
    encryptionSpec Property Map
    Customer-managed encryption key spec for a DataLabelingJob. If set, this DataLabelingJob will be secured by this key. Note: Annotations created in the DataLabelingJob are associated with the EncryptionSpec of the Dataset they are exported to.
    labels Map<String>
    The labels with user-defined metadata to organize your DataLabelingJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each DataLabelingJob: * "aiplatform.googleapis.com/schema": output only, its value is the inputs_schema's title.
    location String
    project String
    specialistPools List<String>
    The SpecialistPools' resource names associated with this job.

    Outputs

    All input properties are implicitly available as output properties. Additionally, the DataLabelingJob resource produces the following output properties:

    CreateTime string
    Timestamp when this DataLabelingJob was created.
    CurrentSpend Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleTypeMoneyResponse
    Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.
    Error Pulumi.GoogleNative.Aiplatform.V1.Outputs.GoogleRpcStatusResponse
    DataLabelingJob errors. It is only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    Id string
    The provider-assigned unique ID for this managed resource.
    LabelingProgress int
    Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.
    Name string
    Resource name of the DataLabelingJob.
    State string
    The detailed state of the job.
    UpdateTime string
    Timestamp when this DataLabelingJob was updated most recently.
    CreateTime string
    Timestamp when this DataLabelingJob was created.
    CurrentSpend GoogleTypeMoneyResponse
    Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.
    Error GoogleRpcStatusResponse
    DataLabelingJob errors. It is only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    Id string
    The provider-assigned unique ID for this managed resource.
    LabelingProgress int
    Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.
    Name string
    Resource name of the DataLabelingJob.
    State string
    The detailed state of the job.
    UpdateTime string
    Timestamp when this DataLabelingJob was updated most recently.
    createTime String
    Timestamp when this DataLabelingJob was created.
    currentSpend GoogleTypeMoneyResponse
    Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.
    error GoogleRpcStatusResponse
    DataLabelingJob errors. It is only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    id String
    The provider-assigned unique ID for this managed resource.
    labelingProgress Integer
    Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.
    name String
    Resource name of the DataLabelingJob.
    state String
    The detailed state of the job.
    updateTime String
    Timestamp when this DataLabelingJob was updated most recently.
    createTime string
    Timestamp when this DataLabelingJob was created.
    currentSpend GoogleTypeMoneyResponse
    Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.
    error GoogleRpcStatusResponse
    DataLabelingJob errors. It is only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    id string
    The provider-assigned unique ID for this managed resource.
    labelingProgress number
    Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.
    name string
    Resource name of the DataLabelingJob.
    state string
    The detailed state of the job.
    updateTime string
    Timestamp when this DataLabelingJob was updated most recently.
    create_time str
    Timestamp when this DataLabelingJob was created.
    current_spend GoogleTypeMoneyResponse
    Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.
    error GoogleRpcStatusResponse
    DataLabelingJob errors. It is only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    id str
    The provider-assigned unique ID for this managed resource.
    labeling_progress int
    Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.
    name str
    Resource name of the DataLabelingJob.
    state str
    The detailed state of the job.
    update_time str
    Timestamp when this DataLabelingJob was updated most recently.
    createTime String
    Timestamp when this DataLabelingJob was created.
    currentSpend Property Map
    Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.
    error Property Map
    DataLabelingJob errors. It is only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    id String
    The provider-assigned unique ID for this managed resource.
    labelingProgress Number
    Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.
    name String
    Resource name of the DataLabelingJob.
    state String
    The detailed state of the job.
    updateTime String
    Timestamp when this DataLabelingJob was updated most recently.

    Supporting Types

    GoogleCloudAiplatformV1ActiveLearningConfig, GoogleCloudAiplatformV1ActiveLearningConfigArgs

    MaxDataItemCount string
    Max number of human labeled DataItems.
    MaxDataItemPercentage int
    Max percent of total DataItems for human labeling.
    SampleConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SampleConfig
    Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
    TrainingConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1TrainingConfig
    CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
    MaxDataItemCount string
    Max number of human labeled DataItems.
    MaxDataItemPercentage int
    Max percent of total DataItems for human labeling.
    SampleConfig GoogleCloudAiplatformV1SampleConfig
    Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
    TrainingConfig GoogleCloudAiplatformV1TrainingConfig
    CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
    maxDataItemCount String
    Max number of human labeled DataItems.
    maxDataItemPercentage Integer
    Max percent of total DataItems for human labeling.
    sampleConfig GoogleCloudAiplatformV1SampleConfig
    Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
    trainingConfig GoogleCloudAiplatformV1TrainingConfig
    CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
    maxDataItemCount string
    Max number of human labeled DataItems.
    maxDataItemPercentage number
    Max percent of total DataItems for human labeling.
    sampleConfig GoogleCloudAiplatformV1SampleConfig
    Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
    trainingConfig GoogleCloudAiplatformV1TrainingConfig
    CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
    max_data_item_count str
    Max number of human labeled DataItems.
    max_data_item_percentage int
    Max percent of total DataItems for human labeling.
    sample_config GoogleCloudAiplatformV1SampleConfig
    Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
    training_config GoogleCloudAiplatformV1TrainingConfig
    CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
    maxDataItemCount String
    Max number of human labeled DataItems.
    maxDataItemPercentage Number
    Max percent of total DataItems for human labeling.
    sampleConfig Property Map
    Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
    trainingConfig Property Map
    CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.

    GoogleCloudAiplatformV1ActiveLearningConfigResponse, GoogleCloudAiplatformV1ActiveLearningConfigResponseArgs

    MaxDataItemCount string
    Max number of human labeled DataItems.
    MaxDataItemPercentage int
    Max percent of total DataItems for human labeling.
    SampleConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SampleConfigResponse
    Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
    TrainingConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1TrainingConfigResponse
    CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
    MaxDataItemCount string
    Max number of human labeled DataItems.
    MaxDataItemPercentage int
    Max percent of total DataItems for human labeling.
    SampleConfig GoogleCloudAiplatformV1SampleConfigResponse
    Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
    TrainingConfig GoogleCloudAiplatformV1TrainingConfigResponse
    CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
    maxDataItemCount String
    Max number of human labeled DataItems.
    maxDataItemPercentage Integer
    Max percent of total DataItems for human labeling.
    sampleConfig GoogleCloudAiplatformV1SampleConfigResponse
    Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
    trainingConfig GoogleCloudAiplatformV1TrainingConfigResponse
    CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
    maxDataItemCount string
    Max number of human labeled DataItems.
    maxDataItemPercentage number
    Max percent of total DataItems for human labeling.
    sampleConfig GoogleCloudAiplatformV1SampleConfigResponse
    Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
    trainingConfig GoogleCloudAiplatformV1TrainingConfigResponse
    CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
    max_data_item_count str
    Max number of human labeled DataItems.
    max_data_item_percentage int
    Max percent of total DataItems for human labeling.
    sample_config GoogleCloudAiplatformV1SampleConfigResponse
    Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
    training_config GoogleCloudAiplatformV1TrainingConfigResponse
    CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.
    maxDataItemCount String
    Max number of human labeled DataItems.
    maxDataItemPercentage Number
    Max percent of total DataItems for human labeling.
    sampleConfig Property Map
    Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy.
    trainingConfig Property Map
    CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems.

    GoogleCloudAiplatformV1EncryptionSpec, GoogleCloudAiplatformV1EncryptionSpecArgs

    KmsKeyName string
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    KmsKeyName string
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    kmsKeyName String
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    kmsKeyName string
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    kms_key_name str
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    kmsKeyName String
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    GoogleCloudAiplatformV1EncryptionSpecResponse, GoogleCloudAiplatformV1EncryptionSpecResponseArgs

    KmsKeyName string
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    KmsKeyName string
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    kmsKeyName String
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    kmsKeyName string
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    kms_key_name str
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
    kmsKeyName String
    The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

    GoogleCloudAiplatformV1SampleConfig, GoogleCloudAiplatformV1SampleConfigArgs

    FollowingBatchSamplePercentage int
    The percentage of data needed to be labeled in each following batch (except the first batch).
    InitialBatchSamplePercentage int
    The percentage of data needed to be labeled in the first batch.
    SampleStrategy Pulumi.GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1SampleConfigSampleStrategy
    Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
    FollowingBatchSamplePercentage int
    The percentage of data needed to be labeled in each following batch (except the first batch).
    InitialBatchSamplePercentage int
    The percentage of data needed to be labeled in the first batch.
    SampleStrategy GoogleCloudAiplatformV1SampleConfigSampleStrategy
    Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
    followingBatchSamplePercentage Integer
    The percentage of data needed to be labeled in each following batch (except the first batch).
    initialBatchSamplePercentage Integer
    The percentage of data needed to be labeled in the first batch.
    sampleStrategy GoogleCloudAiplatformV1SampleConfigSampleStrategy
    Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
    followingBatchSamplePercentage number
    The percentage of data needed to be labeled in each following batch (except the first batch).
    initialBatchSamplePercentage number
    The percentage of data needed to be labeled in the first batch.
    sampleStrategy GoogleCloudAiplatformV1SampleConfigSampleStrategy
    Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
    following_batch_sample_percentage int
    The percentage of data needed to be labeled in each following batch (except the first batch).
    initial_batch_sample_percentage int
    The percentage of data needed to be labeled in the first batch.
    sample_strategy GoogleCloudAiplatformV1SampleConfigSampleStrategy
    Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
    followingBatchSamplePercentage Number
    The percentage of data needed to be labeled in each following batch (except the first batch).
    initialBatchSamplePercentage Number
    The percentage of data needed to be labeled in the first batch.
    sampleStrategy "SAMPLE_STRATEGY_UNSPECIFIED" | "UNCERTAINTY"
    Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.

    GoogleCloudAiplatformV1SampleConfigResponse, GoogleCloudAiplatformV1SampleConfigResponseArgs

    FollowingBatchSamplePercentage int
    The percentage of data needed to be labeled in each following batch (except the first batch).
    InitialBatchSamplePercentage int
    The percentage of data needed to be labeled in the first batch.
    SampleStrategy string
    Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
    FollowingBatchSamplePercentage int
    The percentage of data needed to be labeled in each following batch (except the first batch).
    InitialBatchSamplePercentage int
    The percentage of data needed to be labeled in the first batch.
    SampleStrategy string
    Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
    followingBatchSamplePercentage Integer
    The percentage of data needed to be labeled in each following batch (except the first batch).
    initialBatchSamplePercentage Integer
    The percentage of data needed to be labeled in the first batch.
    sampleStrategy String
    Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
    followingBatchSamplePercentage number
    The percentage of data needed to be labeled in each following batch (except the first batch).
    initialBatchSamplePercentage number
    The percentage of data needed to be labeled in the first batch.
    sampleStrategy string
    Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
    following_batch_sample_percentage int
    The percentage of data needed to be labeled in each following batch (except the first batch).
    initial_batch_sample_percentage int
    The percentage of data needed to be labeled in the first batch.
    sample_strategy str
    Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.
    followingBatchSamplePercentage Number
    The percentage of data needed to be labeled in each following batch (except the first batch).
    initialBatchSamplePercentage Number
    The percentage of data needed to be labeled in the first batch.
    sampleStrategy String
    Field to choose sampling strategy. Sampling strategy will decide which data should be selected for human labeling in every batch.

    GoogleCloudAiplatformV1SampleConfigSampleStrategy, GoogleCloudAiplatformV1SampleConfigSampleStrategyArgs

    SampleStrategyUnspecified
    SAMPLE_STRATEGY_UNSPECIFIEDDefault will be treated as UNCERTAINTY.
    Uncertainty
    UNCERTAINTYSample the most uncertain data to label.
    GoogleCloudAiplatformV1SampleConfigSampleStrategySampleStrategyUnspecified
    SAMPLE_STRATEGY_UNSPECIFIEDDefault will be treated as UNCERTAINTY.
    GoogleCloudAiplatformV1SampleConfigSampleStrategyUncertainty
    UNCERTAINTYSample the most uncertain data to label.
    SampleStrategyUnspecified
    SAMPLE_STRATEGY_UNSPECIFIEDDefault will be treated as UNCERTAINTY.
    Uncertainty
    UNCERTAINTYSample the most uncertain data to label.
    SampleStrategyUnspecified
    SAMPLE_STRATEGY_UNSPECIFIEDDefault will be treated as UNCERTAINTY.
    Uncertainty
    UNCERTAINTYSample the most uncertain data to label.
    SAMPLE_STRATEGY_UNSPECIFIED
    SAMPLE_STRATEGY_UNSPECIFIEDDefault will be treated as UNCERTAINTY.
    UNCERTAINTY
    UNCERTAINTYSample the most uncertain data to label.
    "SAMPLE_STRATEGY_UNSPECIFIED"
    SAMPLE_STRATEGY_UNSPECIFIEDDefault will be treated as UNCERTAINTY.
    "UNCERTAINTY"
    UNCERTAINTYSample the most uncertain data to label.

    GoogleCloudAiplatformV1TrainingConfig, GoogleCloudAiplatformV1TrainingConfigArgs

    TimeoutTrainingMilliHours string
    The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.
    TimeoutTrainingMilliHours string
    The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.
    timeoutTrainingMilliHours String
    The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.
    timeoutTrainingMilliHours string
    The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.
    timeout_training_milli_hours str
    The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.
    timeoutTrainingMilliHours String
    The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.

    GoogleCloudAiplatformV1TrainingConfigResponse, GoogleCloudAiplatformV1TrainingConfigResponseArgs

    TimeoutTrainingMilliHours string
    The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.
    TimeoutTrainingMilliHours string
    The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.
    timeoutTrainingMilliHours String
    The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.
    timeoutTrainingMilliHours string
    The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.
    timeout_training_milli_hours str
    The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.
    timeoutTrainingMilliHours String
    The timeout hours for the CMLE training job, expressed in milli hours i.e. 1,000 value in this field means 1 hour.

    GoogleRpcStatusResponse, GoogleRpcStatusResponseArgs

    Code int
    The status code, which should be an enum value of google.rpc.Code.
    Details List<ImmutableDictionary<string, string>>
    A list of messages that carry the error details. There is a common set of message types for APIs to use.
    Message string
    A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
    Code int
    The status code, which should be an enum value of google.rpc.Code.
    Details []map[string]string
    A list of messages that carry the error details. There is a common set of message types for APIs to use.
    Message string
    A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
    code Integer
    The status code, which should be an enum value of google.rpc.Code.
    details List<Map<String,String>>
    A list of messages that carry the error details. There is a common set of message types for APIs to use.
    message String
    A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
    code number
    The status code, which should be an enum value of google.rpc.Code.
    details {[key: string]: string}[]
    A list of messages that carry the error details. There is a common set of message types for APIs to use.
    message string
    A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
    code int
    The status code, which should be an enum value of google.rpc.Code.
    details Sequence[Mapping[str, str]]
    A list of messages that carry the error details. There is a common set of message types for APIs to use.
    message str
    A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
    code Number
    The status code, which should be an enum value of google.rpc.Code.
    details List<Map<String>>
    A list of messages that carry the error details. There is a common set of message types for APIs to use.
    message String
    A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

    GoogleTypeMoneyResponse, GoogleTypeMoneyResponseArgs

    CurrencyCode string
    The three-letter currency code defined in ISO 4217.
    Nanos int
    Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If units is positive, nanos must be positive or zero. If units is zero, nanos can be positive, zero, or negative. If units is negative, nanos must be negative or zero. For example $-1.75 is represented as units=-1 and nanos=-750,000,000.
    Units string
    The whole units of the amount. For example if currencyCode is "USD", then 1 unit is one US dollar.
    CurrencyCode string
    The three-letter currency code defined in ISO 4217.
    Nanos int
    Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If units is positive, nanos must be positive or zero. If units is zero, nanos can be positive, zero, or negative. If units is negative, nanos must be negative or zero. For example $-1.75 is represented as units=-1 and nanos=-750,000,000.
    Units string
    The whole units of the amount. For example if currencyCode is "USD", then 1 unit is one US dollar.
    currencyCode String
    The three-letter currency code defined in ISO 4217.
    nanos Integer
    Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If units is positive, nanos must be positive or zero. If units is zero, nanos can be positive, zero, or negative. If units is negative, nanos must be negative or zero. For example $-1.75 is represented as units=-1 and nanos=-750,000,000.
    units String
    The whole units of the amount. For example if currencyCode is "USD", then 1 unit is one US dollar.
    currencyCode string
    The three-letter currency code defined in ISO 4217.
    nanos number
    Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If units is positive, nanos must be positive or zero. If units is zero, nanos can be positive, zero, or negative. If units is negative, nanos must be negative or zero. For example $-1.75 is represented as units=-1 and nanos=-750,000,000.
    units string
    The whole units of the amount. For example if currencyCode is "USD", then 1 unit is one US dollar.
    currency_code str
    The three-letter currency code defined in ISO 4217.
    nanos int
    Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If units is positive, nanos must be positive or zero. If units is zero, nanos can be positive, zero, or negative. If units is negative, nanos must be negative or zero. For example $-1.75 is represented as units=-1 and nanos=-750,000,000.
    units str
    The whole units of the amount. For example if currencyCode is "USD", then 1 unit is one US dollar.
    currencyCode String
    The three-letter currency code defined in ISO 4217.
    nanos Number
    Number of nano (10^-9) units of the amount. The value must be between -999,999,999 and +999,999,999 inclusive. If units is positive, nanos must be positive or zero. If units is zero, nanos can be positive, zero, or negative. If units is negative, nanos must be negative or zero. For example $-1.75 is represented as units=-1 and nanos=-750,000,000.
    units String
    The whole units of the amount. For example if currencyCode is "USD", then 1 unit is one US dollar.

    Package Details

    Repository
    Google Cloud Native pulumi/pulumi-google-native
    License
    Apache-2.0
    google-native logo

    Google Cloud Native is in preview. Google Cloud Classic is fully supported.

    Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
    pulumi/pulumi-google-native