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

google-native.aiplatform/v1beta1.BatchPredictionJob

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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
pulumi/pulumi-google-native

    Creates a BatchPredictionJob. A BatchPredictionJob once created will right away be attempted to start. Auto-naming is currently not supported for this resource.

    Create BatchPredictionJob Resource

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

    Constructor syntax

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

@overload
def BatchPredictionJob(resource_name: str,
                       opts: Optional[ResourceOptions] = None,
                       input_config: Optional[GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigArgs] = None,
                       output_config: Optional[GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigArgs] = None,
                       display_name: Optional[str] = None,
                       labels: Optional[Mapping[str, str]] = None,
                       manual_batch_tuning_parameters: Optional[GoogleCloudAiplatformV1beta1ManualBatchTuningParametersArgs] = None,
                       generate_explanation: Optional[bool] = None,
                       encryption_spec: Optional[GoogleCloudAiplatformV1beta1EncryptionSpecArgs] = None,
                       instance_config: Optional[GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigArgs] = None,
                       dedicated_resources: Optional[GoogleCloudAiplatformV1beta1BatchDedicatedResourcesArgs] = None,
                       location: Optional[str] = None,
                       explanation_spec: Optional[GoogleCloudAiplatformV1beta1ExplanationSpecArgs] = None,
                       model: Optional[str] = None,
                       model_monitoring_config: Optional[GoogleCloudAiplatformV1beta1ModelMonitoringConfigArgs] = None,
                       model_monitoring_stats_anomalies: Optional[Sequence[GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesArgs]] = None,
                       model_parameters: Optional[Any] = None,
                       disable_container_logging: Optional[bool] = None,
                       project: Optional[str] = None,
                       service_account: Optional[str] = None,
                       unmanaged_container_model: Optional[GoogleCloudAiplatformV1beta1UnmanagedContainerModelArgs] = None)
    func NewBatchPredictionJob(ctx *Context, name string, args BatchPredictionJobArgs, opts ...ResourceOption) (*BatchPredictionJob, error)
    public BatchPredictionJob(string name, BatchPredictionJobArgs args, CustomResourceOptions? opts = null)
    public BatchPredictionJob(String name, BatchPredictionJobArgs args)
public BatchPredictionJob(String name, BatchPredictionJobArgs args, CustomResourceOptions options)

    type: google-native:aiplatform/v1beta1:BatchPredictionJob
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 BatchPredictionJobArgs
    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 BatchPredictionJobArgs
    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 BatchPredictionJobArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args BatchPredictionJobArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args BatchPredictionJobArgs
    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 google_nativeBatchPredictionJobResource = new GoogleNative.Aiplatform.V1Beta1.BatchPredictionJob("google-nativeBatchPredictionJobResource", new()
{
    InputConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigArgs
    {
        InstancesFormat = "string",
        BigquerySource = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQuerySourceArgs
        {
            InputUri = "string",
        },
        GcsSource = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceArgs
        {
            Uris = new[]
            {
                "string",
            },
        },
    },
    OutputConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigArgs
    {
        PredictionsFormat = "string",
        BigqueryDestination = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestinationArgs
        {
            OutputUri = "string",
        },
        GcsDestination = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationArgs
        {
            OutputUriPrefix = "string",
        },
    },
    DisplayName = "string",
    Labels = 
    {
        { "string", "string" },
    },
    ManualBatchTuningParameters = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ManualBatchTuningParametersArgs
    {
        BatchSize = 0,
    },
    GenerateExplanation = false,
    EncryptionSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EncryptionSpecArgs
    {
        KmsKeyName = "string",
    },
    InstanceConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigArgs
    {
        ExcludedFields = new[]
        {
            "string",
        },
        IncludedFields = new[]
        {
            "string",
        },
        InstanceType = "string",
        KeyField = "string",
    },
    DedicatedResources = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BatchDedicatedResourcesArgs
    {
        MachineSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MachineSpecArgs
        {
            AcceleratorCount = 0,
            AcceleratorType = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType.AcceleratorTypeUnspecified,
            MachineType = "string",
            TpuTopology = "string",
        },
        MaxReplicaCount = 0,
        StartingReplicaCount = 0,
    },
    Location = "string",
    ExplanationSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationSpecArgs
    {
        Parameters = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationParametersArgs
        {
            Examples = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExamplesArgs
            {
                ExampleGcsSource = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceArgs
                {
                    DataFormat = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormat.DataFormatUnspecified,
                    GcsSource = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceArgs
                    {
                        Uris = new[]
                        {
                            "string",
                        },
                    },
                },
                GcsSource = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceArgs
                {
                    Uris = new[]
                    {
                        "string",
                    },
                },
                NearestNeighborSearchConfig = "any",
                NeighborCount = 0,
                Presets = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PresetsArgs
                {
                    Modality = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1PresetsModality.ModalityUnspecified,
                    Query = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1PresetsQuery.Precise,
                },
            },
            IntegratedGradientsAttribution = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionArgs
            {
                StepCount = 0,
                BlurBaselineConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs
                {
                    MaxBlurSigma = 0,
                },
                SmoothGradConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SmoothGradConfigArgs
                {
                    FeatureNoiseSigma = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs
                    {
                        NoiseSigma = new[]
                        {
                            new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs
                            {
                                Name = "string",
                                Sigma = 0,
                            },
                        },
                    },
                    NoiseSigma = 0,
                    NoisySampleCount = 0,
                },
            },
            OutputIndices = new[]
            {
                "any",
            },
            SampledShapleyAttribution = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SampledShapleyAttributionArgs
            {
                PathCount = 0,
            },
            TopK = 0,
            XraiAttribution = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1XraiAttributionArgs
            {
                StepCount = 0,
                BlurBaselineConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs
                {
                    MaxBlurSigma = 0,
                },
                SmoothGradConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SmoothGradConfigArgs
                {
                    FeatureNoiseSigma = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs
                    {
                        NoiseSigma = new[]
                        {
                            new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs
                            {
                                Name = "string",
                                Sigma = 0,
                            },
                        },
                    },
                    NoiseSigma = 0,
                    NoisySampleCount = 0,
                },
            },
        },
        Metadata = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationMetadataArgs
        {
            Inputs = 
            {
                { "string", "string" },
            },
            Outputs = 
            {
                { "string", "string" },
            },
            FeatureAttributionsSchemaUri = "string",
            LatentSpaceSource = "string",
        },
    },
    Model = "string",
    ModelMonitoringConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringConfigArgs
    {
        AlertConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigArgs
        {
            EmailAlertConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigArgs
            {
                UserEmails = new[]
                {
                    "string",
                },
            },
            EnableLogging = false,
            NotificationChannels = new[]
            {
                "string",
            },
        },
        AnalysisInstanceSchemaUri = "string",
        ObjectiveConfigs = new[]
        {
            new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigArgs
            {
                ExplanationConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigArgs
                {
                    EnableFeatureAttributes = false,
                    ExplanationBaseline = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineArgs
                    {
                        Bigquery = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestinationArgs
                        {
                            OutputUri = "string",
                        },
                        Gcs = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationArgs
                        {
                            OutputUriPrefix = "string",
                        },
                        PredictionFormat = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat.PredictionFormatUnspecified,
                    },
                },
                PredictionDriftDetectionConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigArgs
                {
                    AttributionScoreDriftThresholds = 
                    {
                        { "string", "string" },
                    },
                    DefaultDriftThreshold = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfigArgs
                    {
                        Value = 0,
                    },
                    DriftThresholds = 
                    {
                        { "string", "string" },
                    },
                },
                TrainingDataset = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetArgs
                {
                    BigquerySource = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQuerySourceArgs
                    {
                        InputUri = "string",
                    },
                    DataFormat = "string",
                    Dataset = "string",
                    GcsSource = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceArgs
                    {
                        Uris = new[]
                        {
                            "string",
                        },
                    },
                    LoggingSamplingStrategy = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategyArgs
                    {
                        RandomSampleConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigArgs
                        {
                            SampleRate = 0,
                        },
                    },
                    TargetField = "string",
                },
                TrainingPredictionSkewDetectionConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigArgs
                {
                    AttributionScoreSkewThresholds = 
                    {
                        { "string", "string" },
                    },
                    DefaultSkewThreshold = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfigArgs
                    {
                        Value = 0,
                    },
                    SkewThresholds = 
                    {
                        { "string", "string" },
                    },
                },
            },
        },
        StatsAnomaliesBaseDirectory = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationArgs
        {
            OutputUriPrefix = "string",
        },
    },
    ModelMonitoringStatsAnomalies = new[]
    {
        new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesArgs
        {
            AnomalyCount = 0,
            DeployedModelId = "string",
            FeatureStats = new[]
            {
                new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesArgs
                {
                    FeatureDisplayName = "string",
                    PredictionStats = new[]
                    {
                        new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureStatsAnomalyArgs
                        {
                            AnomalyDetectionThreshold = 0,
                            AnomalyUri = "string",
                            DistributionDeviation = 0,
                            EndTime = "string",
                            Score = 0,
                            StartTime = "string",
                            StatsUri = "string",
                        },
                    },
                    Threshold = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfigArgs
                    {
                        Value = 0,
                    },
                    TrainingStats = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureStatsAnomalyArgs
                    {
                        AnomalyDetectionThreshold = 0,
                        AnomalyUri = "string",
                        DistributionDeviation = 0,
                        EndTime = "string",
                        Score = 0,
                        StartTime = "string",
                        StatsUri = "string",
                    },
                },
            },
            Objective = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjective.ModelDeploymentMonitoringObjectiveTypeUnspecified,
        },
    },
    ModelParameters = "any",
    DisableContainerLogging = false,
    Project = "string",
    ServiceAccount = "string",
    UnmanagedContainerModel = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1UnmanagedContainerModelArgs
    {
        ArtifactUri = "string",
        ContainerSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelContainerSpecArgs
        {
            ImageUri = "string",
            Args = new[]
            {
                "string",
            },
            Command = new[]
            {
                "string",
            },
            DeploymentTimeout = "string",
            Env = new[]
            {
                new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EnvVarArgs
                {
                    Name = "string",
                    Value = "string",
                },
            },
            HealthProbe = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeArgs
            {
                Exec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeExecActionArgs
                {
                    Command = new[]
                    {
                        "string",
                    },
                },
                PeriodSeconds = 0,
                TimeoutSeconds = 0,
            },
            HealthRoute = "string",
            Ports = new[]
            {
                new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PortArgs
                {
                    ContainerPort = 0,
                },
            },
            PredictRoute = "string",
            SharedMemorySizeMb = "string",
            StartupProbe = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeArgs
            {
                Exec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeExecActionArgs
                {
                    Command = new[]
                    {
                        "string",
                    },
                },
                PeriodSeconds = 0,
                TimeoutSeconds = 0,
            },
        },
        PredictSchemata = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PredictSchemataArgs
        {
            InstanceSchemaUri = "string",
            ParametersSchemaUri = "string",
            PredictionSchemaUri = "string",
        },
    },
});

    example, err := aiplatformv1beta1.NewBatchPredictionJob(ctx, "google-nativeBatchPredictionJobResource", &aiplatformv1beta1.BatchPredictionJobArgs{
	InputConfig: &aiplatform.GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigArgs{
		InstancesFormat: pulumi.String("string"),
		BigquerySource: &aiplatform.GoogleCloudAiplatformV1beta1BigQuerySourceArgs{
			InputUri: pulumi.String("string"),
		},
		GcsSource: &aiplatform.GoogleCloudAiplatformV1beta1GcsSourceArgs{
			Uris: pulumi.StringArray{
				pulumi.String("string"),
			},
		},
	},
	OutputConfig: &aiplatform.GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigArgs{
		PredictionsFormat: pulumi.String("string"),
		BigqueryDestination: &aiplatform.GoogleCloudAiplatformV1beta1BigQueryDestinationArgs{
			OutputUri: pulumi.String("string"),
		},
		GcsDestination: &aiplatform.GoogleCloudAiplatformV1beta1GcsDestinationArgs{
			OutputUriPrefix: pulumi.String("string"),
		},
	},
	DisplayName: pulumi.String("string"),
	Labels: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	ManualBatchTuningParameters: &aiplatform.GoogleCloudAiplatformV1beta1ManualBatchTuningParametersArgs{
		BatchSize: pulumi.Int(0),
	},
	GenerateExplanation: pulumi.Bool(false),
	EncryptionSpec: &aiplatform.GoogleCloudAiplatformV1beta1EncryptionSpecArgs{
		KmsKeyName: pulumi.String("string"),
	},
	InstanceConfig: &aiplatform.GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigArgs{
		ExcludedFields: pulumi.StringArray{
			pulumi.String("string"),
		},
		IncludedFields: pulumi.StringArray{
			pulumi.String("string"),
		},
		InstanceType: pulumi.String("string"),
		KeyField:     pulumi.String("string"),
	},
	DedicatedResources: &aiplatform.GoogleCloudAiplatformV1beta1BatchDedicatedResourcesArgs{
		MachineSpec: &aiplatform.GoogleCloudAiplatformV1beta1MachineSpecArgs{
			AcceleratorCount: pulumi.Int(0),
			AcceleratorType:  aiplatformv1beta1.GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeAcceleratorTypeUnspecified,
			MachineType:      pulumi.String("string"),
			TpuTopology:      pulumi.String("string"),
		},
		MaxReplicaCount:      pulumi.Int(0),
		StartingReplicaCount: pulumi.Int(0),
	},
	Location: pulumi.String("string"),
	ExplanationSpec: &aiplatform.GoogleCloudAiplatformV1beta1ExplanationSpecArgs{
		Parameters: &aiplatform.GoogleCloudAiplatformV1beta1ExplanationParametersArgs{
			Examples: &aiplatform.GoogleCloudAiplatformV1beta1ExamplesArgs{
				ExampleGcsSource: &aiplatform.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceArgs{
					DataFormat: aiplatformv1beta1.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormatDataFormatUnspecified,
					GcsSource: &aiplatform.GoogleCloudAiplatformV1beta1GcsSourceArgs{
						Uris: pulumi.StringArray{
							pulumi.String("string"),
						},
					},
				},
				GcsSource: &aiplatform.GoogleCloudAiplatformV1beta1GcsSourceArgs{
					Uris: pulumi.StringArray{
						pulumi.String("string"),
					},
				},
				NearestNeighborSearchConfig: pulumi.Any("any"),
				NeighborCount:               pulumi.Int(0),
				Presets: &aiplatform.GoogleCloudAiplatformV1beta1PresetsArgs{
					Modality: aiplatformv1beta1.GoogleCloudAiplatformV1beta1PresetsModalityModalityUnspecified,
					Query:    aiplatformv1beta1.GoogleCloudAiplatformV1beta1PresetsQueryPrecise,
				},
			},
			IntegratedGradientsAttribution: &aiplatform.GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionArgs{
				StepCount: pulumi.Int(0),
				BlurBaselineConfig: &aiplatform.GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs{
					MaxBlurSigma: pulumi.Float64(0),
				},
				SmoothGradConfig: &aiplatform.GoogleCloudAiplatformV1beta1SmoothGradConfigArgs{
					FeatureNoiseSigma: &aiplatform.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs{
						NoiseSigma: aiplatform.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArray{
							&aiplatform.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs{
								Name:  pulumi.String("string"),
								Sigma: pulumi.Float64(0),
							},
						},
					},
					NoiseSigma:       pulumi.Float64(0),
					NoisySampleCount: pulumi.Int(0),
				},
			},
			OutputIndices: pulumi.Array{
				pulumi.Any("any"),
			},
			SampledShapleyAttribution: &aiplatform.GoogleCloudAiplatformV1beta1SampledShapleyAttributionArgs{
				PathCount: pulumi.Int(0),
			},
			TopK: pulumi.Int(0),
			XraiAttribution: &aiplatform.GoogleCloudAiplatformV1beta1XraiAttributionArgs{
				StepCount: pulumi.Int(0),
				BlurBaselineConfig: &aiplatform.GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs{
					MaxBlurSigma: pulumi.Float64(0),
				},
				SmoothGradConfig: &aiplatform.GoogleCloudAiplatformV1beta1SmoothGradConfigArgs{
					FeatureNoiseSigma: &aiplatform.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs{
						NoiseSigma: aiplatform.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArray{
							&aiplatform.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs{
								Name:  pulumi.String("string"),
								Sigma: pulumi.Float64(0),
							},
						},
					},
					NoiseSigma:       pulumi.Float64(0),
					NoisySampleCount: pulumi.Int(0),
				},
			},
		},
		Metadata: &aiplatform.GoogleCloudAiplatformV1beta1ExplanationMetadataArgs{
			Inputs: pulumi.StringMap{
				"string": pulumi.String("string"),
			},
			Outputs: pulumi.StringMap{
				"string": pulumi.String("string"),
			},
			FeatureAttributionsSchemaUri: pulumi.String("string"),
			LatentSpaceSource:            pulumi.String("string"),
		},
	},
	Model: pulumi.String("string"),
	ModelMonitoringConfig: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringConfigArgs{
		AlertConfig: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigArgs{
			EmailAlertConfig: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigArgs{
				UserEmails: pulumi.StringArray{
					pulumi.String("string"),
				},
			},
			EnableLogging: pulumi.Bool(false),
			NotificationChannels: pulumi.StringArray{
				pulumi.String("string"),
			},
		},
		AnalysisInstanceSchemaUri: pulumi.String("string"),
		ObjectiveConfigs: aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigArray{
			&aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigArgs{
				ExplanationConfig: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigArgs{
					EnableFeatureAttributes: pulumi.Bool(false),
					ExplanationBaseline: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineArgs{
						Bigquery: &aiplatform.GoogleCloudAiplatformV1beta1BigQueryDestinationArgs{
							OutputUri: pulumi.String("string"),
						},
						Gcs: &aiplatform.GoogleCloudAiplatformV1beta1GcsDestinationArgs{
							OutputUriPrefix: pulumi.String("string"),
						},
						PredictionFormat: aiplatformv1beta1.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormatPredictionFormatUnspecified,
					},
				},
				PredictionDriftDetectionConfig: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigArgs{
					AttributionScoreDriftThresholds: pulumi.StringMap{
						"string": pulumi.String("string"),
					},
					DefaultDriftThreshold: &aiplatform.GoogleCloudAiplatformV1beta1ThresholdConfigArgs{
						Value: pulumi.Float64(0),
					},
					DriftThresholds: pulumi.StringMap{
						"string": pulumi.String("string"),
					},
				},
				TrainingDataset: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetArgs{
					BigquerySource: &aiplatform.GoogleCloudAiplatformV1beta1BigQuerySourceArgs{
						InputUri: pulumi.String("string"),
					},
					DataFormat: pulumi.String("string"),
					Dataset:    pulumi.String("string"),
					GcsSource: &aiplatform.GoogleCloudAiplatformV1beta1GcsSourceArgs{
						Uris: pulumi.StringArray{
							pulumi.String("string"),
						},
					},
					LoggingSamplingStrategy: &aiplatform.GoogleCloudAiplatformV1beta1SamplingStrategyArgs{
						RandomSampleConfig: &aiplatform.GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigArgs{
							SampleRate: pulumi.Float64(0),
						},
					},
					TargetField: pulumi.String("string"),
				},
				TrainingPredictionSkewDetectionConfig: &aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigArgs{
					AttributionScoreSkewThresholds: pulumi.StringMap{
						"string": pulumi.String("string"),
					},
					DefaultSkewThreshold: &aiplatform.GoogleCloudAiplatformV1beta1ThresholdConfigArgs{
						Value: pulumi.Float64(0),
					},
					SkewThresholds: pulumi.StringMap{
						"string": pulumi.String("string"),
					},
				},
			},
		},
		StatsAnomaliesBaseDirectory: &aiplatform.GoogleCloudAiplatformV1beta1GcsDestinationArgs{
			OutputUriPrefix: pulumi.String("string"),
		},
	},
	ModelMonitoringStatsAnomalies: aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesArray{
		&aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesArgs{
			AnomalyCount:    pulumi.Int(0),
			DeployedModelId: pulumi.String("string"),
			FeatureStats: aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesArray{
				&aiplatform.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesArgs{
					FeatureDisplayName: pulumi.String("string"),
					PredictionStats: aiplatform.GoogleCloudAiplatformV1beta1FeatureStatsAnomalyArray{
						&aiplatform.GoogleCloudAiplatformV1beta1FeatureStatsAnomalyArgs{
							AnomalyDetectionThreshold: pulumi.Float64(0),
							AnomalyUri:                pulumi.String("string"),
							DistributionDeviation:     pulumi.Float64(0),
							EndTime:                   pulumi.String("string"),
							Score:                     pulumi.Float64(0),
							StartTime:                 pulumi.String("string"),
							StatsUri:                  pulumi.String("string"),
						},
					},
					Threshold: &aiplatform.GoogleCloudAiplatformV1beta1ThresholdConfigArgs{
						Value: pulumi.Float64(0),
					},
					TrainingStats: &aiplatform.GoogleCloudAiplatformV1beta1FeatureStatsAnomalyArgs{
						AnomalyDetectionThreshold: pulumi.Float64(0),
						AnomalyUri:                pulumi.String("string"),
						DistributionDeviation:     pulumi.Float64(0),
						EndTime:                   pulumi.String("string"),
						Score:                     pulumi.Float64(0),
						StartTime:                 pulumi.String("string"),
						StatsUri:                  pulumi.String("string"),
					},
				},
			},
			Objective: aiplatformv1beta1.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjectiveModelDeploymentMonitoringObjectiveTypeUnspecified,
		},
	},
	ModelParameters:         pulumi.Any("any"),
	DisableContainerLogging: pulumi.Bool(false),
	Project:                 pulumi.String("string"),
	ServiceAccount:          pulumi.String("string"),
	UnmanagedContainerModel: &aiplatform.GoogleCloudAiplatformV1beta1UnmanagedContainerModelArgs{
		ArtifactUri: pulumi.String("string"),
		ContainerSpec: &aiplatform.GoogleCloudAiplatformV1beta1ModelContainerSpecArgs{
			ImageUri: pulumi.String("string"),
			Args: pulumi.StringArray{
				pulumi.String("string"),
			},
			Command: pulumi.StringArray{
				pulumi.String("string"),
			},
			DeploymentTimeout: pulumi.String("string"),
			Env: aiplatform.GoogleCloudAiplatformV1beta1EnvVarArray{
				&aiplatform.GoogleCloudAiplatformV1beta1EnvVarArgs{
					Name:  pulumi.String("string"),
					Value: pulumi.String("string"),
				},
			},
			HealthProbe: &aiplatform.GoogleCloudAiplatformV1beta1ProbeArgs{
				Exec: &aiplatform.GoogleCloudAiplatformV1beta1ProbeExecActionArgs{
					Command: pulumi.StringArray{
						pulumi.String("string"),
					},
				},
				PeriodSeconds:  pulumi.Int(0),
				TimeoutSeconds: pulumi.Int(0),
			},
			HealthRoute: pulumi.String("string"),
			Ports: aiplatform.GoogleCloudAiplatformV1beta1PortArray{
				&aiplatform.GoogleCloudAiplatformV1beta1PortArgs{
					ContainerPort: pulumi.Int(0),
				},
			},
			PredictRoute:       pulumi.String("string"),
			SharedMemorySizeMb: pulumi.String("string"),
			StartupProbe: &aiplatform.GoogleCloudAiplatformV1beta1ProbeArgs{
				Exec: &aiplatform.GoogleCloudAiplatformV1beta1ProbeExecActionArgs{
					Command: pulumi.StringArray{
						pulumi.String("string"),
					},
				},
				PeriodSeconds:  pulumi.Int(0),
				TimeoutSeconds: pulumi.Int(0),
			},
		},
		PredictSchemata: &aiplatform.GoogleCloudAiplatformV1beta1PredictSchemataArgs{
			InstanceSchemaUri:   pulumi.String("string"),
			ParametersSchemaUri: pulumi.String("string"),
			PredictionSchemaUri: pulumi.String("string"),
		},
	},
})

    var google_nativeBatchPredictionJobResource = new BatchPredictionJob("google-nativeBatchPredictionJobResource", BatchPredictionJobArgs.builder()
    .inputConfig(GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigArgs.builder()
        .instancesFormat("string")
        .bigquerySource(GoogleCloudAiplatformV1beta1BigQuerySourceArgs.builder()
            .inputUri("string")
            .build())
        .gcsSource(GoogleCloudAiplatformV1beta1GcsSourceArgs.builder()
            .uris("string")
            .build())
        .build())
    .outputConfig(GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigArgs.builder()
        .predictionsFormat("string")
        .bigqueryDestination(GoogleCloudAiplatformV1beta1BigQueryDestinationArgs.builder()
            .outputUri("string")
            .build())
        .gcsDestination(GoogleCloudAiplatformV1beta1GcsDestinationArgs.builder()
            .outputUriPrefix("string")
            .build())
        .build())
    .displayName("string")
    .labels(Map.of("string", "string"))
    .manualBatchTuningParameters(GoogleCloudAiplatformV1beta1ManualBatchTuningParametersArgs.builder()
        .batchSize(0)
        .build())
    .generateExplanation(false)
    .encryptionSpec(GoogleCloudAiplatformV1beta1EncryptionSpecArgs.builder()
        .kmsKeyName("string")
        .build())
    .instanceConfig(GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigArgs.builder()
        .excludedFields("string")
        .includedFields("string")
        .instanceType("string")
        .keyField("string")
        .build())
    .dedicatedResources(GoogleCloudAiplatformV1beta1BatchDedicatedResourcesArgs.builder()
        .machineSpec(GoogleCloudAiplatformV1beta1MachineSpecArgs.builder()
            .acceleratorCount(0)
            .acceleratorType("ACCELERATOR_TYPE_UNSPECIFIED")
            .machineType("string")
            .tpuTopology("string")
            .build())
        .maxReplicaCount(0)
        .startingReplicaCount(0)
        .build())
    .location("string")
    .explanationSpec(GoogleCloudAiplatformV1beta1ExplanationSpecArgs.builder()
        .parameters(GoogleCloudAiplatformV1beta1ExplanationParametersArgs.builder()
            .examples(GoogleCloudAiplatformV1beta1ExamplesArgs.builder()
                .exampleGcsSource(GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceArgs.builder()
                    .dataFormat("DATA_FORMAT_UNSPECIFIED")
                    .gcsSource(GoogleCloudAiplatformV1beta1GcsSourceArgs.builder()
                        .uris("string")
                        .build())
                    .build())
                .gcsSource(GoogleCloudAiplatformV1beta1GcsSourceArgs.builder()
                    .uris("string")
                    .build())
                .nearestNeighborSearchConfig("any")
                .neighborCount(0)
                .presets(GoogleCloudAiplatformV1beta1PresetsArgs.builder()
                    .modality("MODALITY_UNSPECIFIED")
                    .query("PRECISE")
                    .build())
                .build())
            .integratedGradientsAttribution(GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionArgs.builder()
                .stepCount(0)
                .blurBaselineConfig(GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs.builder()
                    .maxBlurSigma(0)
                    .build())
                .smoothGradConfig(GoogleCloudAiplatformV1beta1SmoothGradConfigArgs.builder()
                    .featureNoiseSigma(GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs.builder()
                        .noiseSigma(GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs.builder()
                            .name("string")
                            .sigma(0)
                            .build())
                        .build())
                    .noiseSigma(0)
                    .noisySampleCount(0)
                    .build())
                .build())
            .outputIndices("any")
            .sampledShapleyAttribution(GoogleCloudAiplatformV1beta1SampledShapleyAttributionArgs.builder()
                .pathCount(0)
                .build())
            .topK(0)
            .xraiAttribution(GoogleCloudAiplatformV1beta1XraiAttributionArgs.builder()
                .stepCount(0)
                .blurBaselineConfig(GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs.builder()
                    .maxBlurSigma(0)
                    .build())
                .smoothGradConfig(GoogleCloudAiplatformV1beta1SmoothGradConfigArgs.builder()
                    .featureNoiseSigma(GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs.builder()
                        .noiseSigma(GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs.builder()
                            .name("string")
                            .sigma(0)
                            .build())
                        .build())
                    .noiseSigma(0)
                    .noisySampleCount(0)
                    .build())
                .build())
            .build())
        .metadata(GoogleCloudAiplatformV1beta1ExplanationMetadataArgs.builder()
            .inputs(Map.of("string", "string"))
            .outputs(Map.of("string", "string"))
            .featureAttributionsSchemaUri("string")
            .latentSpaceSource("string")
            .build())
        .build())
    .model("string")
    .modelMonitoringConfig(GoogleCloudAiplatformV1beta1ModelMonitoringConfigArgs.builder()
        .alertConfig(GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigArgs.builder()
            .emailAlertConfig(GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigArgs.builder()
                .userEmails("string")
                .build())
            .enableLogging(false)
            .notificationChannels("string")
            .build())
        .analysisInstanceSchemaUri("string")
        .objectiveConfigs(GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigArgs.builder()
            .explanationConfig(GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigArgs.builder()
                .enableFeatureAttributes(false)
                .explanationBaseline(GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineArgs.builder()
                    .bigquery(GoogleCloudAiplatformV1beta1BigQueryDestinationArgs.builder()
                        .outputUri("string")
                        .build())
                    .gcs(GoogleCloudAiplatformV1beta1GcsDestinationArgs.builder()
                        .outputUriPrefix("string")
                        .build())
                    .predictionFormat("PREDICTION_FORMAT_UNSPECIFIED")
                    .build())
                .build())
            .predictionDriftDetectionConfig(GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigArgs.builder()
                .attributionScoreDriftThresholds(Map.of("string", "string"))
                .defaultDriftThreshold(GoogleCloudAiplatformV1beta1ThresholdConfigArgs.builder()
                    .value(0)
                    .build())
                .driftThresholds(Map.of("string", "string"))
                .build())
            .trainingDataset(GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetArgs.builder()
                .bigquerySource(GoogleCloudAiplatformV1beta1BigQuerySourceArgs.builder()
                    .inputUri("string")
                    .build())
                .dataFormat("string")
                .dataset("string")
                .gcsSource(GoogleCloudAiplatformV1beta1GcsSourceArgs.builder()
                    .uris("string")
                    .build())
                .loggingSamplingStrategy(GoogleCloudAiplatformV1beta1SamplingStrategyArgs.builder()
                    .randomSampleConfig(GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigArgs.builder()
                        .sampleRate(0)
                        .build())
                    .build())
                .targetField("string")
                .build())
            .trainingPredictionSkewDetectionConfig(GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigArgs.builder()
                .attributionScoreSkewThresholds(Map.of("string", "string"))
                .defaultSkewThreshold(GoogleCloudAiplatformV1beta1ThresholdConfigArgs.builder()
                    .value(0)
                    .build())
                .skewThresholds(Map.of("string", "string"))
                .build())
            .build())
        .statsAnomaliesBaseDirectory(GoogleCloudAiplatformV1beta1GcsDestinationArgs.builder()
            .outputUriPrefix("string")
            .build())
        .build())
    .modelMonitoringStatsAnomalies(GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesArgs.builder()
        .anomalyCount(0)
        .deployedModelId("string")
        .featureStats(GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesArgs.builder()
            .featureDisplayName("string")
            .predictionStats(GoogleCloudAiplatformV1beta1FeatureStatsAnomalyArgs.builder()
                .anomalyDetectionThreshold(0)
                .anomalyUri("string")
                .distributionDeviation(0)
                .endTime("string")
                .score(0)
                .startTime("string")
                .statsUri("string")
                .build())
            .threshold(GoogleCloudAiplatformV1beta1ThresholdConfigArgs.builder()
                .value(0)
                .build())
            .trainingStats(GoogleCloudAiplatformV1beta1FeatureStatsAnomalyArgs.builder()
                .anomalyDetectionThreshold(0)
                .anomalyUri("string")
                .distributionDeviation(0)
                .endTime("string")
                .score(0)
                .startTime("string")
                .statsUri("string")
                .build())
            .build())
        .objective("MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED")
        .build())
    .modelParameters("any")
    .disableContainerLogging(false)
    .project("string")
    .serviceAccount("string")
    .unmanagedContainerModel(GoogleCloudAiplatformV1beta1UnmanagedContainerModelArgs.builder()
        .artifactUri("string")
        .containerSpec(GoogleCloudAiplatformV1beta1ModelContainerSpecArgs.builder()
            .imageUri("string")
            .args("string")
            .command("string")
            .deploymentTimeout("string")
            .env(GoogleCloudAiplatformV1beta1EnvVarArgs.builder()
                .name("string")
                .value("string")
                .build())
            .healthProbe(GoogleCloudAiplatformV1beta1ProbeArgs.builder()
                .exec(GoogleCloudAiplatformV1beta1ProbeExecActionArgs.builder()
                    .command("string")
                    .build())
                .periodSeconds(0)
                .timeoutSeconds(0)
                .build())
            .healthRoute("string")
            .ports(GoogleCloudAiplatformV1beta1PortArgs.builder()
                .containerPort(0)
                .build())
            .predictRoute("string")
            .sharedMemorySizeMb("string")
            .startupProbe(GoogleCloudAiplatformV1beta1ProbeArgs.builder()
                .exec(GoogleCloudAiplatformV1beta1ProbeExecActionArgs.builder()
                    .command("string")
                    .build())
                .periodSeconds(0)
                .timeoutSeconds(0)
                .build())
            .build())
        .predictSchemata(GoogleCloudAiplatformV1beta1PredictSchemataArgs.builder()
            .instanceSchemaUri("string")
            .parametersSchemaUri("string")
            .predictionSchemaUri("string")
            .build())
        .build())
    .build());

    google_native_batch_prediction_job_resource = google_native.aiplatform.v1beta1.BatchPredictionJob("google-nativeBatchPredictionJobResource",
    input_config={
        "instances_format": "string",
        "bigquery_source": {
            "input_uri": "string",
        },
        "gcs_source": {
            "uris": ["string"],
        },
    },
    output_config={
        "predictions_format": "string",
        "bigquery_destination": {
            "output_uri": "string",
        },
        "gcs_destination": {
            "output_uri_prefix": "string",
        },
    },
    display_name="string",
    labels={
        "string": "string",
    },
    manual_batch_tuning_parameters={
        "batch_size": 0,
    },
    generate_explanation=False,
    encryption_spec={
        "kms_key_name": "string",
    },
    instance_config={
        "excluded_fields": ["string"],
        "included_fields": ["string"],
        "instance_type": "string",
        "key_field": "string",
    },
    dedicated_resources={
        "machine_spec": {
            "accelerator_count": 0,
            "accelerator_type": google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType.ACCELERATOR_TYPE_UNSPECIFIED,
            "machine_type": "string",
            "tpu_topology": "string",
        },
        "max_replica_count": 0,
        "starting_replica_count": 0,
    },
    location="string",
    explanation_spec={
        "parameters": {
            "examples": {
                "example_gcs_source": {
                    "data_format": google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormat.DATA_FORMAT_UNSPECIFIED,
                    "gcs_source": {
                        "uris": ["string"],
                    },
                },
                "gcs_source": {
                    "uris": ["string"],
                },
                "nearest_neighbor_search_config": "any",
                "neighbor_count": 0,
                "presets": {
                    "modality": google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1PresetsModality.MODALITY_UNSPECIFIED,
                    "query": google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1PresetsQuery.PRECISE,
                },
            },
            "integrated_gradients_attribution": {
                "step_count": 0,
                "blur_baseline_config": {
                    "max_blur_sigma": 0,
                },
                "smooth_grad_config": {
                    "feature_noise_sigma": {
                        "noise_sigma": [{
                            "name": "string",
                            "sigma": 0,
                        }],
                    },
                    "noise_sigma": 0,
                    "noisy_sample_count": 0,
                },
            },
            "output_indices": ["any"],
            "sampled_shapley_attribution": {
                "path_count": 0,
            },
            "top_k": 0,
            "xrai_attribution": {
                "step_count": 0,
                "blur_baseline_config": {
                    "max_blur_sigma": 0,
                },
                "smooth_grad_config": {
                    "feature_noise_sigma": {
                        "noise_sigma": [{
                            "name": "string",
                            "sigma": 0,
                        }],
                    },
                    "noise_sigma": 0,
                    "noisy_sample_count": 0,
                },
            },
        },
        "metadata": {
            "inputs": {
                "string": "string",
            },
            "outputs": {
                "string": "string",
            },
            "feature_attributions_schema_uri": "string",
            "latent_space_source": "string",
        },
    },
    model="string",
    model_monitoring_config={
        "alert_config": {
            "email_alert_config": {
                "user_emails": ["string"],
            },
            "enable_logging": False,
            "notification_channels": ["string"],
        },
        "analysis_instance_schema_uri": "string",
        "objective_configs": [{
            "explanation_config": {
                "enable_feature_attributes": False,
                "explanation_baseline": {
                    "bigquery": {
                        "output_uri": "string",
                    },
                    "gcs": {
                        "output_uri_prefix": "string",
                    },
                    "prediction_format": google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat.PREDICTION_FORMAT_UNSPECIFIED,
                },
            },
            "prediction_drift_detection_config": {
                "attribution_score_drift_thresholds": {
                    "string": "string",
                },
                "default_drift_threshold": {
                    "value": 0,
                },
                "drift_thresholds": {
                    "string": "string",
                },
            },
            "training_dataset": {
                "bigquery_source": {
                    "input_uri": "string",
                },
                "data_format": "string",
                "dataset": "string",
                "gcs_source": {
                    "uris": ["string"],
                },
                "logging_sampling_strategy": {
                    "random_sample_config": {
                        "sample_rate": 0,
                    },
                },
                "target_field": "string",
            },
            "training_prediction_skew_detection_config": {
                "attribution_score_skew_thresholds": {
                    "string": "string",
                },
                "default_skew_threshold": {
                    "value": 0,
                },
                "skew_thresholds": {
                    "string": "string",
                },
            },
        }],
        "stats_anomalies_base_directory": {
            "output_uri_prefix": "string",
        },
    },
    model_monitoring_stats_anomalies=[{
        "anomaly_count": 0,
        "deployed_model_id": "string",
        "feature_stats": [{
            "feature_display_name": "string",
            "prediction_stats": [{
                "anomaly_detection_threshold": 0,
                "anomaly_uri": "string",
                "distribution_deviation": 0,
                "end_time": "string",
                "score": 0,
                "start_time": "string",
                "stats_uri": "string",
            }],
            "threshold": {
                "value": 0,
            },
            "training_stats": {
                "anomaly_detection_threshold": 0,
                "anomaly_uri": "string",
                "distribution_deviation": 0,
                "end_time": "string",
                "score": 0,
                "start_time": "string",
                "stats_uri": "string",
            },
        }],
        "objective": google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjective.MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED,
    }],
    model_parameters="any",
    disable_container_logging=False,
    project="string",
    service_account="string",
    unmanaged_container_model={
        "artifact_uri": "string",
        "container_spec": {
            "image_uri": "string",
            "args": ["string"],
            "command": ["string"],
            "deployment_timeout": "string",
            "env": [{
                "name": "string",
                "value": "string",
            }],
            "health_probe": {
                "exec_": {
                    "command": ["string"],
                },
                "period_seconds": 0,
                "timeout_seconds": 0,
            },
            "health_route": "string",
            "ports": [{
                "container_port": 0,
            }],
            "predict_route": "string",
            "shared_memory_size_mb": "string",
            "startup_probe": {
                "exec_": {
                    "command": ["string"],
                },
                "period_seconds": 0,
                "timeout_seconds": 0,
            },
        },
        "predict_schemata": {
            "instance_schema_uri": "string",
            "parameters_schema_uri": "string",
            "prediction_schema_uri": "string",
        },
    })

    const google_nativeBatchPredictionJobResource = new google_native.aiplatform.v1beta1.BatchPredictionJob("google-nativeBatchPredictionJobResource", {
    inputConfig: {
        instancesFormat: "string",
        bigquerySource: {
            inputUri: "string",
        },
        gcsSource: {
            uris: ["string"],
        },
    },
    outputConfig: {
        predictionsFormat: "string",
        bigqueryDestination: {
            outputUri: "string",
        },
        gcsDestination: {
            outputUriPrefix: "string",
        },
    },
    displayName: "string",
    labels: {
        string: "string",
    },
    manualBatchTuningParameters: {
        batchSize: 0,
    },
    generateExplanation: false,
    encryptionSpec: {
        kmsKeyName: "string",
    },
    instanceConfig: {
        excludedFields: ["string"],
        includedFields: ["string"],
        instanceType: "string",
        keyField: "string",
    },
    dedicatedResources: {
        machineSpec: {
            acceleratorCount: 0,
            acceleratorType: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType.AcceleratorTypeUnspecified,
            machineType: "string",
            tpuTopology: "string",
        },
        maxReplicaCount: 0,
        startingReplicaCount: 0,
    },
    location: "string",
    explanationSpec: {
        parameters: {
            examples: {
                exampleGcsSource: {
                    dataFormat: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormat.DataFormatUnspecified,
                    gcsSource: {
                        uris: ["string"],
                    },
                },
                gcsSource: {
                    uris: ["string"],
                },
                nearestNeighborSearchConfig: "any",
                neighborCount: 0,
                presets: {
                    modality: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1PresetsModality.ModalityUnspecified,
                    query: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1PresetsQuery.Precise,
                },
            },
            integratedGradientsAttribution: {
                stepCount: 0,
                blurBaselineConfig: {
                    maxBlurSigma: 0,
                },
                smoothGradConfig: {
                    featureNoiseSigma: {
                        noiseSigma: [{
                            name: "string",
                            sigma: 0,
                        }],
                    },
                    noiseSigma: 0,
                    noisySampleCount: 0,
                },
            },
            outputIndices: ["any"],
            sampledShapleyAttribution: {
                pathCount: 0,
            },
            topK: 0,
            xraiAttribution: {
                stepCount: 0,
                blurBaselineConfig: {
                    maxBlurSigma: 0,
                },
                smoothGradConfig: {
                    featureNoiseSigma: {
                        noiseSigma: [{
                            name: "string",
                            sigma: 0,
                        }],
                    },
                    noiseSigma: 0,
                    noisySampleCount: 0,
                },
            },
        },
        metadata: {
            inputs: {
                string: "string",
            },
            outputs: {
                string: "string",
            },
            featureAttributionsSchemaUri: "string",
            latentSpaceSource: "string",
        },
    },
    model: "string",
    modelMonitoringConfig: {
        alertConfig: {
            emailAlertConfig: {
                userEmails: ["string"],
            },
            enableLogging: false,
            notificationChannels: ["string"],
        },
        analysisInstanceSchemaUri: "string",
        objectiveConfigs: [{
            explanationConfig: {
                enableFeatureAttributes: false,
                explanationBaseline: {
                    bigquery: {
                        outputUri: "string",
                    },
                    gcs: {
                        outputUriPrefix: "string",
                    },
                    predictionFormat: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat.PredictionFormatUnspecified,
                },
            },
            predictionDriftDetectionConfig: {
                attributionScoreDriftThresholds: {
                    string: "string",
                },
                defaultDriftThreshold: {
                    value: 0,
                },
                driftThresholds: {
                    string: "string",
                },
            },
            trainingDataset: {
                bigquerySource: {
                    inputUri: "string",
                },
                dataFormat: "string",
                dataset: "string",
                gcsSource: {
                    uris: ["string"],
                },
                loggingSamplingStrategy: {
                    randomSampleConfig: {
                        sampleRate: 0,
                    },
                },
                targetField: "string",
            },
            trainingPredictionSkewDetectionConfig: {
                attributionScoreSkewThresholds: {
                    string: "string",
                },
                defaultSkewThreshold: {
                    value: 0,
                },
                skewThresholds: {
                    string: "string",
                },
            },
        }],
        statsAnomaliesBaseDirectory: {
            outputUriPrefix: "string",
        },
    },
    modelMonitoringStatsAnomalies: [{
        anomalyCount: 0,
        deployedModelId: "string",
        featureStats: [{
            featureDisplayName: "string",
            predictionStats: [{
                anomalyDetectionThreshold: 0,
                anomalyUri: "string",
                distributionDeviation: 0,
                endTime: "string",
                score: 0,
                startTime: "string",
                statsUri: "string",
            }],
            threshold: {
                value: 0,
            },
            trainingStats: {
                anomalyDetectionThreshold: 0,
                anomalyUri: "string",
                distributionDeviation: 0,
                endTime: "string",
                score: 0,
                startTime: "string",
                statsUri: "string",
            },
        }],
        objective: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjective.ModelDeploymentMonitoringObjectiveTypeUnspecified,
    }],
    modelParameters: "any",
    disableContainerLogging: false,
    project: "string",
    serviceAccount: "string",
    unmanagedContainerModel: {
        artifactUri: "string",
        containerSpec: {
            imageUri: "string",
            args: ["string"],
            command: ["string"],
            deploymentTimeout: "string",
            env: [{
                name: "string",
                value: "string",
            }],
            healthProbe: {
                exec: {
                    command: ["string"],
                },
                periodSeconds: 0,
                timeoutSeconds: 0,
            },
            healthRoute: "string",
            ports: [{
                containerPort: 0,
            }],
            predictRoute: "string",
            sharedMemorySizeMb: "string",
            startupProbe: {
                exec: {
                    command: ["string"],
                },
                periodSeconds: 0,
                timeoutSeconds: 0,
            },
        },
        predictSchemata: {
            instanceSchemaUri: "string",
            parametersSchemaUri: "string",
            predictionSchemaUri: "string",
        },
    },
});

    type: google-native:aiplatform/v1beta1:BatchPredictionJob
properties:
    dedicatedResources:
        machineSpec:
            acceleratorCount: 0
            acceleratorType: ACCELERATOR_TYPE_UNSPECIFIED
            machineType: string
            tpuTopology: string
        maxReplicaCount: 0
        startingReplicaCount: 0
    disableContainerLogging: false
    displayName: string
    encryptionSpec:
        kmsKeyName: string
    explanationSpec:
        metadata:
            featureAttributionsSchemaUri: string
            inputs:
                string: string
            latentSpaceSource: string
            outputs:
                string: string
        parameters:
            examples:
                exampleGcsSource:
                    dataFormat: DATA_FORMAT_UNSPECIFIED
                    gcsSource:
                        uris:
                            - string
                gcsSource:
                    uris:
                        - string
                nearestNeighborSearchConfig: any
                neighborCount: 0
                presets:
                    modality: MODALITY_UNSPECIFIED
                    query: PRECISE
            integratedGradientsAttribution:
                blurBaselineConfig:
                    maxBlurSigma: 0
                smoothGradConfig:
                    featureNoiseSigma:
                        noiseSigma:
                            - name: string
                              sigma: 0
                    noiseSigma: 0
                    noisySampleCount: 0
                stepCount: 0
            outputIndices:
                - any
            sampledShapleyAttribution:
                pathCount: 0
            topK: 0
            xraiAttribution:
                blurBaselineConfig:
                    maxBlurSigma: 0
                smoothGradConfig:
                    featureNoiseSigma:
                        noiseSigma:
                            - name: string
                              sigma: 0
                    noiseSigma: 0
                    noisySampleCount: 0
                stepCount: 0
    generateExplanation: false
    inputConfig:
        bigquerySource:
            inputUri: string
        gcsSource:
            uris:
                - string
        instancesFormat: string
    instanceConfig:
        excludedFields:
            - string
        includedFields:
            - string
        instanceType: string
        keyField: string
    labels:
        string: string
    location: string
    manualBatchTuningParameters:
        batchSize: 0
    model: string
    modelMonitoringConfig:
        alertConfig:
            emailAlertConfig:
                userEmails:
                    - string
            enableLogging: false
            notificationChannels:
                - string
        analysisInstanceSchemaUri: string
        objectiveConfigs:
            - explanationConfig:
                enableFeatureAttributes: false
                explanationBaseline:
                    bigquery:
                        outputUri: string
                    gcs:
                        outputUriPrefix: string
                    predictionFormat: PREDICTION_FORMAT_UNSPECIFIED
              predictionDriftDetectionConfig:
                attributionScoreDriftThresholds:
                    string: string
                defaultDriftThreshold:
                    value: 0
                driftThresholds:
                    string: string
              trainingDataset:
                bigquerySource:
                    inputUri: string
                dataFormat: string
                dataset: string
                gcsSource:
                    uris:
                        - string
                loggingSamplingStrategy:
                    randomSampleConfig:
                        sampleRate: 0
                targetField: string
              trainingPredictionSkewDetectionConfig:
                attributionScoreSkewThresholds:
                    string: string
                defaultSkewThreshold:
                    value: 0
                skewThresholds:
                    string: string
        statsAnomaliesBaseDirectory:
            outputUriPrefix: string
    modelMonitoringStatsAnomalies:
        - anomalyCount: 0
          deployedModelId: string
          featureStats:
            - featureDisplayName: string
              predictionStats:
                - anomalyDetectionThreshold: 0
                  anomalyUri: string
                  distributionDeviation: 0
                  endTime: string
                  score: 0
                  startTime: string
                  statsUri: string
              threshold:
                value: 0
              trainingStats:
                anomalyDetectionThreshold: 0
                anomalyUri: string
                distributionDeviation: 0
                endTime: string
                score: 0
                startTime: string
                statsUri: string
          objective: MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED
    modelParameters: any
    outputConfig:
        bigqueryDestination:
            outputUri: string
        gcsDestination:
            outputUriPrefix: string
        predictionsFormat: string
    project: string
    serviceAccount: string
    unmanagedContainerModel:
        artifactUri: string
        containerSpec:
            args:
                - string
            command:
                - string
            deploymentTimeout: string
            env:
                - name: string
                  value: string
            healthProbe:
                exec:
                    command:
                        - string
                periodSeconds: 0
                timeoutSeconds: 0
            healthRoute: string
            imageUri: string
            ports:
                - containerPort: 0
            predictRoute: string
            sharedMemorySizeMb: string
            startupProbe:
                exec:
                    command:
                        - string
                periodSeconds: 0
                timeoutSeconds: 0
        predictSchemata:
            instanceSchemaUri: string
            parametersSchemaUri: string
            predictionSchemaUri: string

    BatchPredictionJob 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 BatchPredictionJob resource accepts the following input properties:

    DisplayName string
    The user-defined name of this BatchPredictionJob.
    InputConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfig
    Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
    OutputConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfig
    The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
    DedicatedResources Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BatchDedicatedResources
    The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
    DisableContainerLogging bool
    For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
    EncryptionSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EncryptionSpec
    Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
    ExplanationSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationSpec
    Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
    GenerateExplanation bool
    Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
    InstanceConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfig
    Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
    Labels Dictionary<string, string>
    The labels with user-defined metadata to organize BatchPredictionJobs. 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.
    Location string
    ManualBatchTuningParameters Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ManualBatchTuningParameters
    Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
    Model string
    The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
    ModelMonitoringConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringConfig
    Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset.
    ModelMonitoringStatsAnomalies List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomalies>
    Get batch prediction job monitoring statistics.
    ModelParameters object
    The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
    Project string
    ServiceAccount string
    The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
    UnmanagedContainerModel Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1UnmanagedContainerModel
    Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
    DisplayName string
    The user-defined name of this BatchPredictionJob.
    InputConfig GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigArgs
    Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
    OutputConfig GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigArgs
    The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
    DedicatedResources GoogleCloudAiplatformV1beta1BatchDedicatedResourcesArgs
    The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
    DisableContainerLogging bool
    For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
    EncryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecArgs
    Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
    ExplanationSpec GoogleCloudAiplatformV1beta1ExplanationSpecArgs
    Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
    GenerateExplanation bool
    Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
    InstanceConfig GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigArgs
    Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
    Labels map[string]string
    The labels with user-defined metadata to organize BatchPredictionJobs. 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.
    Location string
    ManualBatchTuningParameters GoogleCloudAiplatformV1beta1ManualBatchTuningParametersArgs
    Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
    Model string
    The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
    ModelMonitoringConfig GoogleCloudAiplatformV1beta1ModelMonitoringConfigArgs
    Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset.
    ModelMonitoringStatsAnomalies []GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesArgs
    Get batch prediction job monitoring statistics.
    ModelParameters interface{}
    The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
    Project string
    ServiceAccount string
    The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
    UnmanagedContainerModel GoogleCloudAiplatformV1beta1UnmanagedContainerModelArgs
    Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
    displayName String
    The user-defined name of this BatchPredictionJob.
    inputConfig GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfig
    Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
    outputConfig GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfig
    The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
    dedicatedResources GoogleCloudAiplatformV1beta1BatchDedicatedResources
    The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
    disableContainerLogging Boolean
    For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
    encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpec
    Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
    explanationSpec GoogleCloudAiplatformV1beta1ExplanationSpec
    Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
    generateExplanation Boolean
    Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
    instanceConfig GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfig
    Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
    labels Map<String,String>
    The labels with user-defined metadata to organize BatchPredictionJobs. 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.
    location String
    manualBatchTuningParameters GoogleCloudAiplatformV1beta1ManualBatchTuningParameters
    Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
    model String
    The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
    modelMonitoringConfig GoogleCloudAiplatformV1beta1ModelMonitoringConfig
    Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset.
    modelMonitoringStatsAnomalies List<GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomalies>
    Get batch prediction job monitoring statistics.
    modelParameters Object
    The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
    project String
    serviceAccount String
    The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
    unmanagedContainerModel GoogleCloudAiplatformV1beta1UnmanagedContainerModel
    Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
    displayName string
    The user-defined name of this BatchPredictionJob.
    inputConfig GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfig
    Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
    outputConfig GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfig
    The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
    dedicatedResources GoogleCloudAiplatformV1beta1BatchDedicatedResources
    The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
    disableContainerLogging boolean
    For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
    encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpec
    Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
    explanationSpec GoogleCloudAiplatformV1beta1ExplanationSpec
    Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
    generateExplanation boolean
    Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
    instanceConfig GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfig
    Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
    labels {[key: string]: string}
    The labels with user-defined metadata to organize BatchPredictionJobs. 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.
    location string
    manualBatchTuningParameters GoogleCloudAiplatformV1beta1ManualBatchTuningParameters
    Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
    model string
    The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
    modelMonitoringConfig GoogleCloudAiplatformV1beta1ModelMonitoringConfig
    Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset.
    modelMonitoringStatsAnomalies GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomalies[]
    Get batch prediction job monitoring statistics.
    modelParameters any
    The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
    project string
    serviceAccount string
    The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
    unmanagedContainerModel GoogleCloudAiplatformV1beta1UnmanagedContainerModel
    Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
    display_name str
    The user-defined name of this BatchPredictionJob.
    input_config GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigArgs
    Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
    output_config GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigArgs
    The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
    dedicated_resources GoogleCloudAiplatformV1beta1BatchDedicatedResourcesArgs
    The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
    disable_container_logging bool
    For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
    encryption_spec GoogleCloudAiplatformV1beta1EncryptionSpecArgs
    Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
    explanation_spec GoogleCloudAiplatformV1beta1ExplanationSpecArgs
    Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
    generate_explanation bool
    Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
    instance_config GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigArgs
    Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
    labels Mapping[str, str]
    The labels with user-defined metadata to organize BatchPredictionJobs. 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.
    location str
    manual_batch_tuning_parameters GoogleCloudAiplatformV1beta1ManualBatchTuningParametersArgs
    Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
    model str
    The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
    model_monitoring_config GoogleCloudAiplatformV1beta1ModelMonitoringConfigArgs
    Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset.
    model_monitoring_stats_anomalies Sequence[GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesArgs]
    Get batch prediction job monitoring statistics.
    model_parameters Any
    The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
    project str
    service_account str
    The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
    unmanaged_container_model GoogleCloudAiplatformV1beta1UnmanagedContainerModelArgs
    Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.
    displayName String
    The user-defined name of this BatchPredictionJob.
    inputConfig Property Map
    Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
    outputConfig Property Map
    The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
    dedicatedResources Property Map
    The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
    disableContainerLogging Boolean
    For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
    encryptionSpec Property Map
    Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
    explanationSpec Property Map
    Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
    generateExplanation Boolean
    Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: * bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. * jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. * csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
    instanceConfig Property Map
    Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
    labels Map<String>
    The labels with user-defined metadata to organize BatchPredictionJobs. 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.
    location String
    manualBatchTuningParameters Property Map
    Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Vertex AI does the tuning itself).
    model String
    The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model and unmanaged_container_model must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
    modelMonitoringConfig Property Map
    Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset.
    modelMonitoringStatsAnomalies List<Property Map>
    Get batch prediction job monitoring statistics.
    modelParameters Any
    The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
    project String
    serviceAccount String
    The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
    unmanagedContainerModel Property Map
    Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model and unmanaged_container_model must be set.

    Outputs

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

    CompletionStats Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1CompletionStatsResponse
    Statistics on completed and failed prediction instances.
    CreateTime string
    Time when the BatchPredictionJob was created.
    EndTime string
    Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
    Error Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleRpcStatusResponse
    Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    Id string
    The provider-assigned unique ID for this managed resource.
    ModelMonitoringStatus Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleRpcStatusResponse
    The running status of the model monitoring pipeline.
    ModelVersionId string
    The version ID of the Model that produces the predictions via this job.
    Name string
    Resource name of the BatchPredictionJob.
    OutputInfo Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfoResponse
    Information further describing the output of this job.
    PartialFailures List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleRpcStatusResponse>
    Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
    ResourcesConsumed Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1ResourcesConsumedResponse
    Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
    StartTime string
    Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
    State string
    The detailed state of the job.
    UpdateTime string
    Time when the BatchPredictionJob was most recently updated.
    CompletionStats GoogleCloudAiplatformV1beta1CompletionStatsResponse
    Statistics on completed and failed prediction instances.
    CreateTime string
    Time when the BatchPredictionJob was created.
    EndTime string
    Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
    Error GoogleRpcStatusResponse
    Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    Id string
    The provider-assigned unique ID for this managed resource.
    ModelMonitoringStatus GoogleRpcStatusResponse
    The running status of the model monitoring pipeline.
    ModelVersionId string
    The version ID of the Model that produces the predictions via this job.
    Name string
    Resource name of the BatchPredictionJob.
    OutputInfo GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfoResponse
    Information further describing the output of this job.
    PartialFailures []GoogleRpcStatusResponse
    Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
    ResourcesConsumed GoogleCloudAiplatformV1beta1ResourcesConsumedResponse
    Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
    StartTime string
    Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
    State string
    The detailed state of the job.
    UpdateTime string
    Time when the BatchPredictionJob was most recently updated.
    completionStats GoogleCloudAiplatformV1beta1CompletionStatsResponse
    Statistics on completed and failed prediction instances.
    createTime String
    Time when the BatchPredictionJob was created.
    endTime String
    Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
    error GoogleRpcStatusResponse
    Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    id String
    The provider-assigned unique ID for this managed resource.
    modelMonitoringStatus GoogleRpcStatusResponse
    The running status of the model monitoring pipeline.
    modelVersionId String
    The version ID of the Model that produces the predictions via this job.
    name String
    Resource name of the BatchPredictionJob.
    outputInfo GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfoResponse
    Information further describing the output of this job.
    partialFailures List<GoogleRpcStatusResponse>
    Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
    resourcesConsumed GoogleCloudAiplatformV1beta1ResourcesConsumedResponse
    Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
    startTime String
    Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
    state String
    The detailed state of the job.
    updateTime String
    Time when the BatchPredictionJob was most recently updated.
    completionStats GoogleCloudAiplatformV1beta1CompletionStatsResponse
    Statistics on completed and failed prediction instances.
    createTime string
    Time when the BatchPredictionJob was created.
    endTime string
    Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
    error GoogleRpcStatusResponse
    Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    id string
    The provider-assigned unique ID for this managed resource.
    modelMonitoringStatus GoogleRpcStatusResponse
    The running status of the model monitoring pipeline.
    modelVersionId string
    The version ID of the Model that produces the predictions via this job.
    name string
    Resource name of the BatchPredictionJob.
    outputInfo GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfoResponse
    Information further describing the output of this job.
    partialFailures GoogleRpcStatusResponse[]
    Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
    resourcesConsumed GoogleCloudAiplatformV1beta1ResourcesConsumedResponse
    Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
    startTime string
    Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
    state string
    The detailed state of the job.
    updateTime string
    Time when the BatchPredictionJob was most recently updated.
    completion_stats GoogleCloudAiplatformV1beta1CompletionStatsResponse
    Statistics on completed and failed prediction instances.
    create_time str
    Time when the BatchPredictionJob was created.
    end_time str
    Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
    error GoogleRpcStatusResponse
    Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    id str
    The provider-assigned unique ID for this managed resource.
    model_monitoring_status GoogleRpcStatusResponse
    The running status of the model monitoring pipeline.
    model_version_id str
    The version ID of the Model that produces the predictions via this job.
    name str
    Resource name of the BatchPredictionJob.
    output_info GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfoResponse
    Information further describing the output of this job.
    partial_failures Sequence[GoogleRpcStatusResponse]
    Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
    resources_consumed GoogleCloudAiplatformV1beta1ResourcesConsumedResponse
    Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
    start_time str
    Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
    state str
    The detailed state of the job.
    update_time str
    Time when the BatchPredictionJob was most recently updated.
    completionStats Property Map
    Statistics on completed and failed prediction instances.
    createTime String
    Time when the BatchPredictionJob was created.
    endTime String
    Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
    error Property Map
    Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
    id String
    The provider-assigned unique ID for this managed resource.
    modelMonitoringStatus Property Map
    The running status of the model monitoring pipeline.
    modelVersionId String
    The version ID of the Model that produces the predictions via this job.
    name String
    Resource name of the BatchPredictionJob.
    outputInfo Property Map
    Information further describing the output of this job.
    partialFailures List<Property Map>
    Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
    resourcesConsumed Property Map
    Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
    startTime String
    Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
    state String
    The detailed state of the job.
    updateTime String
    Time when the BatchPredictionJob was most recently updated.

    Supporting Types

    GoogleCloudAiplatformV1beta1BatchDedicatedResources, GoogleCloudAiplatformV1beta1BatchDedicatedResourcesArgs

    MachineSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MachineSpec
    Immutable. The specification of a single machine.
    MaxReplicaCount int
    Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
    StartingReplicaCount int
    Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
    MachineSpec GoogleCloudAiplatformV1beta1MachineSpec
    Immutable. The specification of a single machine.
    MaxReplicaCount int
    Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
    StartingReplicaCount int
    Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
    machineSpec GoogleCloudAiplatformV1beta1MachineSpec
    Immutable. The specification of a single machine.
    maxReplicaCount Integer
    Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
    startingReplicaCount Integer
    Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
    machineSpec GoogleCloudAiplatformV1beta1MachineSpec
    Immutable. The specification of a single machine.
    maxReplicaCount number
    Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
    startingReplicaCount number
    Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
    machine_spec GoogleCloudAiplatformV1beta1MachineSpec
    Immutable. The specification of a single machine.
    max_replica_count int
    Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
    starting_replica_count int
    Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
    machineSpec Property Map
    Immutable. The specification of a single machine.
    maxReplicaCount Number
    Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
    startingReplicaCount Number
    Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count

    GoogleCloudAiplatformV1beta1BatchDedicatedResourcesResponse, GoogleCloudAiplatformV1beta1BatchDedicatedResourcesResponseArgs

    MachineSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MachineSpecResponse
    Immutable. The specification of a single machine.
    MaxReplicaCount int
    Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
    StartingReplicaCount int
    Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
    MachineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse
    Immutable. The specification of a single machine.
    MaxReplicaCount int
    Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
    StartingReplicaCount int
    Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
    machineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse
    Immutable. The specification of a single machine.
    maxReplicaCount Integer
    Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
    startingReplicaCount Integer
    Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
    machineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse
    Immutable. The specification of a single machine.
    maxReplicaCount number
    Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
    startingReplicaCount number
    Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
    machine_spec GoogleCloudAiplatformV1beta1MachineSpecResponse
    Immutable. The specification of a single machine.
    max_replica_count int
    Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
    starting_replica_count int
    Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
    machineSpec Property Map
    Immutable. The specification of a single machine.
    maxReplicaCount Number
    Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
    startingReplicaCount Number
    Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count

    GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfig, GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigArgs

    InstancesFormat string
    The format in which instances are given, must be one of the Model's supported_input_storage_formats.
    BigquerySource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQuerySource
    The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
    GcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage location for the input instances.
    InstancesFormat string
    The format in which instances are given, must be one of the Model's supported_input_storage_formats.
    BigquerySource GoogleCloudAiplatformV1beta1BigQuerySource
    The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
    GcsSource GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage location for the input instances.
    instancesFormat String
    The format in which instances are given, must be one of the Model's supported_input_storage_formats.
    bigquerySource GoogleCloudAiplatformV1beta1BigQuerySource
    The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
    gcsSource GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage location for the input instances.
    instancesFormat string
    The format in which instances are given, must be one of the Model's supported_input_storage_formats.
    bigquerySource GoogleCloudAiplatformV1beta1BigQuerySource
    The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
    gcsSource GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage location for the input instances.
    instances_format str
    The format in which instances are given, must be one of the Model's supported_input_storage_formats.
    bigquery_source GoogleCloudAiplatformV1beta1BigQuerySource
    The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
    gcs_source GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage location for the input instances.
    instancesFormat String
    The format in which instances are given, must be one of the Model's supported_input_storage_formats.
    bigquerySource Property Map
    The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
    gcsSource Property Map
    The Cloud Storage location for the input instances.

    GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigResponse, GoogleCloudAiplatformV1beta1BatchPredictionJobInputConfigResponseArgs

    BigquerySource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQuerySourceResponse
    The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
    GcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage location for the input instances.
    InstancesFormat string
    The format in which instances are given, must be one of the Model's supported_input_storage_formats.
    BigquerySource GoogleCloudAiplatformV1beta1BigQuerySourceResponse
    The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
    GcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage location for the input instances.
    InstancesFormat string
    The format in which instances are given, must be one of the Model's supported_input_storage_formats.
    bigquerySource GoogleCloudAiplatformV1beta1BigQuerySourceResponse
    The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
    gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage location for the input instances.
    instancesFormat String
    The format in which instances are given, must be one of the Model's supported_input_storage_formats.
    bigquerySource GoogleCloudAiplatformV1beta1BigQuerySourceResponse
    The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
    gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage location for the input instances.
    instancesFormat string
    The format in which instances are given, must be one of the Model's supported_input_storage_formats.
    bigquery_source GoogleCloudAiplatformV1beta1BigQuerySourceResponse
    The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
    gcs_source GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage location for the input instances.
    instances_format str
    The format in which instances are given, must be one of the Model's supported_input_storage_formats.
    bigquerySource Property Map
    The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
    gcsSource Property Map
    The Cloud Storage location for the input instances.
    instancesFormat String
    The format in which instances are given, must be one of the Model's supported_input_storage_formats.

    GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfig, GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigArgs

    ExcludedFields List<string>
    Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    IncludedFields List<string>
    Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    InstanceType string
    The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
    KeyField string
    The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    ExcludedFields []string
    Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    IncludedFields []string
    Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    InstanceType string
    The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
    KeyField string
    The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    excludedFields List<String>
    Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    includedFields List<String>
    Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    instanceType String
    The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
    keyField String
    The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    excludedFields string[]
    Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    includedFields string[]
    Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    instanceType string
    The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
    keyField string
    The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    excluded_fields Sequence[str]
    Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    included_fields Sequence[str]
    Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    instance_type str
    The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
    key_field str
    The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    excludedFields List<String>
    Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    includedFields List<String>
    Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    instanceType String
    The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
    keyField String
    The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

    GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigResponse, GoogleCloudAiplatformV1beta1BatchPredictionJobInstanceConfigResponseArgs

    ExcludedFields List<string>
    Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    IncludedFields List<string>
    Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    InstanceType string
    The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
    KeyField string
    The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    ExcludedFields []string
    Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    IncludedFields []string
    Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    InstanceType string
    The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
    KeyField string
    The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    excludedFields List<String>
    Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    includedFields List<String>
    Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    instanceType String
    The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
    keyField String
    The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    excludedFields string[]
    Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    includedFields string[]
    Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    instanceType string
    The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
    keyField string
    The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    excluded_fields Sequence[str]
    Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    included_fields Sequence[str]
    Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    instance_type str
    The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
    key_field str
    The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    excludedFields List<String>
    Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    includedFields List<String>
    Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
    instanceType String
    The format of the instance that the Model accepts. Vertex AI will convert compatible batch prediction input instance formats to the specified format. Supported values are: * object: Each input is converted to JSON object format. * For bigquery, each row is converted to an object. * For jsonl, each line of the JSONL input must be an object. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. * array: Each input is converted to JSON array format. * For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. * For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. * Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Vertex AI converts the batch prediction input as follows: * For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. * For jsonl, the prediction instance format is determined by each line of the input. * For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": }, where is the Base64-encoded string of the content of the record. * For `file-list`, each file in the list will be converted to an object in the format of `{"b64": }`, where is the Base64-encoded string of the content of the file.
    keyField String
    The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: * For jsonl output format, the output will have a key field instead of the instance field. * For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.

    GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfig, GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigArgs

    PredictionsFormat string
    The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
    BigqueryDestination Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestination
    The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
    GcsDestination Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestination
    The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
    PredictionsFormat string
    The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
    BigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestination
    The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
    GcsDestination GoogleCloudAiplatformV1beta1GcsDestination
    The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
    predictionsFormat String
    The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
    bigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestination
    The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
    gcsDestination GoogleCloudAiplatformV1beta1GcsDestination
    The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
    predictionsFormat string
    The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
    bigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestination
    The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
    gcsDestination GoogleCloudAiplatformV1beta1GcsDestination
    The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
    predictions_format str
    The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
    bigquery_destination GoogleCloudAiplatformV1beta1BigQueryDestination
    The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
    gcs_destination GoogleCloudAiplatformV1beta1GcsDestination
    The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
    predictionsFormat String
    The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
    bigqueryDestination Property Map
    The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
    gcsDestination Property Map
    The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.

    GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigResponse, GoogleCloudAiplatformV1beta1BatchPredictionJobOutputConfigResponseArgs

    BigqueryDestination Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
    The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
    GcsDestination Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationResponse
    The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
    PredictionsFormat string
    The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
    BigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
    The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
    GcsDestination GoogleCloudAiplatformV1beta1GcsDestinationResponse
    The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
    PredictionsFormat string
    The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
    bigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
    The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
    gcsDestination GoogleCloudAiplatformV1beta1GcsDestinationResponse
    The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
    predictionsFormat String
    The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
    bigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
    The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
    gcsDestination GoogleCloudAiplatformV1beta1GcsDestinationResponse
    The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
    predictionsFormat string
    The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
    bigquery_destination GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
    The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
    gcs_destination GoogleCloudAiplatformV1beta1GcsDestinationResponse
    The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
    predictions_format str
    The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.
    bigqueryDestination Property Map
    The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction__ where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
    gcsDestination Property Map
    The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction--, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001., predictions_0002., ..., predictions_N. are created where `` depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001., errors_0002.,..., errors_N. files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
    predictionsFormat String
    The format in which Vertex AI gives the predictions, must be one of the Model's supported_output_storage_formats.

    GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfoResponse, GoogleCloudAiplatformV1beta1BatchPredictionJobOutputInfoResponseArgs

    BigqueryOutputDataset string
    The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
    BigqueryOutputTable string
    The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
    GcsOutputDirectory string
    The full path of the Cloud Storage directory created, into which the prediction output is written.
    BigqueryOutputDataset string
    The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
    BigqueryOutputTable string
    The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
    GcsOutputDirectory string
    The full path of the Cloud Storage directory created, into which the prediction output is written.
    bigqueryOutputDataset String
    The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
    bigqueryOutputTable String
    The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
    gcsOutputDirectory String
    The full path of the Cloud Storage directory created, into which the prediction output is written.
    bigqueryOutputDataset string
    The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
    bigqueryOutputTable string
    The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
    gcsOutputDirectory string
    The full path of the Cloud Storage directory created, into which the prediction output is written.
    bigquery_output_dataset str
    The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
    bigquery_output_table str
    The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
    gcs_output_directory str
    The full path of the Cloud Storage directory created, into which the prediction output is written.
    bigqueryOutputDataset String
    The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
    bigqueryOutputTable String
    The name of the BigQuery table created, in predictions_ format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
    gcsOutputDirectory String
    The full path of the Cloud Storage directory created, into which the prediction output is written.

    GoogleCloudAiplatformV1beta1BigQueryDestination, GoogleCloudAiplatformV1beta1BigQueryDestinationArgs

    OutputUri string
    BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
    OutputUri string
    BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
    outputUri String
    BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
    outputUri string
    BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
    output_uri str
    BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
    outputUri String
    BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.

    GoogleCloudAiplatformV1beta1BigQueryDestinationResponse, GoogleCloudAiplatformV1beta1BigQueryDestinationResponseArgs

    OutputUri string
    BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
    OutputUri string
    BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
    outputUri String
    BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
    outputUri string
    BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
    output_uri str
    BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.
    outputUri String
    BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: * BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.

    GoogleCloudAiplatformV1beta1BigQuerySource, GoogleCloudAiplatformV1beta1BigQuerySourceArgs

    InputUri string
    BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
    InputUri string
    BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
    inputUri String
    BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
    inputUri string
    BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
    input_uri str
    BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
    inputUri String
    BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.

    GoogleCloudAiplatformV1beta1BigQuerySourceResponse, GoogleCloudAiplatformV1beta1BigQuerySourceResponseArgs

    InputUri string
    BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
    InputUri string
    BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
    inputUri String
    BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
    inputUri string
    BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
    input_uri str
    BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.
    inputUri String
    BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.

    GoogleCloudAiplatformV1beta1BlurBaselineConfig, GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs

    MaxBlurSigma double
    The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
    MaxBlurSigma float64
    The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
    maxBlurSigma Double
    The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
    maxBlurSigma number
    The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
    max_blur_sigma float
    The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
    maxBlurSigma Number
    The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.

    GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse, GoogleCloudAiplatformV1beta1BlurBaselineConfigResponseArgs

    MaxBlurSigma double
    The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
    MaxBlurSigma float64
    The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
    maxBlurSigma Double
    The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
    maxBlurSigma number
    The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
    max_blur_sigma float
    The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
    maxBlurSigma Number
    The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.

    GoogleCloudAiplatformV1beta1CompletionStatsResponse, GoogleCloudAiplatformV1beta1CompletionStatsResponseArgs

    FailedCount string
    The number of entities for which any error was encountered.
    IncompleteCount string
    In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
    SuccessfulCount string
    The number of entities that had been processed successfully.
    SuccessfulForecastPointCount string
    The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.
    FailedCount string
    The number of entities for which any error was encountered.
    IncompleteCount string
    In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
    SuccessfulCount string
    The number of entities that had been processed successfully.
    SuccessfulForecastPointCount string
    The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.
    failedCount String
    The number of entities for which any error was encountered.
    incompleteCount String
    In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
    successfulCount String
    The number of entities that had been processed successfully.
    successfulForecastPointCount String
    The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.
    failedCount string
    The number of entities for which any error was encountered.
    incompleteCount string
    In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
    successfulCount string
    The number of entities that had been processed successfully.
    successfulForecastPointCount string
    The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.
    failed_count str
    The number of entities for which any error was encountered.
    incomplete_count str
    In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
    successful_count str
    The number of entities that had been processed successfully.
    successful_forecast_point_count str
    The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.
    failedCount String
    The number of entities for which any error was encountered.
    incompleteCount String
    In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
    successfulCount String
    The number of entities that had been processed successfully.
    successfulForecastPointCount String
    The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.

    GoogleCloudAiplatformV1beta1EncryptionSpec, GoogleCloudAiplatformV1beta1EncryptionSpecArgs

    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.

    GoogleCloudAiplatformV1beta1EncryptionSpecResponse, GoogleCloudAiplatformV1beta1EncryptionSpecResponseArgs

    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.

    GoogleCloudAiplatformV1beta1EnvVar, GoogleCloudAiplatformV1beta1EnvVarArgs

    Name string
    Name of the environment variable. Must be a valid C identifier.
    Value string
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    Name string
    Name of the environment variable. Must be a valid C identifier.
    Value string
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    name String
    Name of the environment variable. Must be a valid C identifier.
    value String
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    name string
    Name of the environment variable. Must be a valid C identifier.
    value string
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    name str
    Name of the environment variable. Must be a valid C identifier.
    value str
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    name String
    Name of the environment variable. Must be a valid C identifier.
    value String
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

    GoogleCloudAiplatformV1beta1EnvVarResponse, GoogleCloudAiplatformV1beta1EnvVarResponseArgs

    Name string
    Name of the environment variable. Must be a valid C identifier.
    Value string
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    Name string
    Name of the environment variable. Must be a valid C identifier.
    Value string
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    name String
    Name of the environment variable. Must be a valid C identifier.
    value String
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    name string
    Name of the environment variable. Must be a valid C identifier.
    value string
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    name str
    Name of the environment variable. Must be a valid C identifier.
    value str
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
    name String
    Name of the environment variable. Must be a valid C identifier.
    value String
    Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

    GoogleCloudAiplatformV1beta1Examples, GoogleCloudAiplatformV1beta1ExamplesArgs

    ExampleGcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSource
    The Cloud Storage input instances.
    GcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
    NearestNeighborSearchConfig object
    The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
    NeighborCount int
    The number of neighbors to return when querying for examples.
    Presets Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1Presets
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    ExampleGcsSource GoogleCloudAiplatformV1beta1ExamplesExampleGcsSource
    The Cloud Storage input instances.
    GcsSource GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
    NearestNeighborSearchConfig interface{}
    The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
    NeighborCount int
    The number of neighbors to return when querying for examples.
    Presets GoogleCloudAiplatformV1beta1Presets
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    exampleGcsSource GoogleCloudAiplatformV1beta1ExamplesExampleGcsSource
    The Cloud Storage input instances.
    gcsSource GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
    nearestNeighborSearchConfig Object
    The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
    neighborCount Integer
    The number of neighbors to return when querying for examples.
    presets GoogleCloudAiplatformV1beta1Presets
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    exampleGcsSource GoogleCloudAiplatformV1beta1ExamplesExampleGcsSource
    The Cloud Storage input instances.
    gcsSource GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
    nearestNeighborSearchConfig any
    The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
    neighborCount number
    The number of neighbors to return when querying for examples.
    presets GoogleCloudAiplatformV1beta1Presets
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    example_gcs_source GoogleCloudAiplatformV1beta1ExamplesExampleGcsSource
    The Cloud Storage input instances.
    gcs_source GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
    nearest_neighbor_search_config Any
    The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
    neighbor_count int
    The number of neighbors to return when querying for examples.
    presets GoogleCloudAiplatformV1beta1Presets
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    exampleGcsSource Property Map
    The Cloud Storage input instances.
    gcsSource Property Map
    The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
    nearestNeighborSearchConfig Any
    The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
    neighborCount Number
    The number of neighbors to return when querying for examples.
    presets Property Map
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

    GoogleCloudAiplatformV1beta1ExamplesExampleGcsSource, GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceArgs

    DataFormat Pulumi.GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormat
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    GcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage location for the input instances.
    DataFormat GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormat
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    GcsSource GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage location for the input instances.
    dataFormat GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormat
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcsSource GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage location for the input instances.
    dataFormat GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormat
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcsSource GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage location for the input instances.
    data_format GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormat
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcs_source GoogleCloudAiplatformV1beta1GcsSource
    The Cloud Storage location for the input instances.
    dataFormat "DATA_FORMAT_UNSPECIFIED" | "JSONL"
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcsSource Property Map
    The Cloud Storage location for the input instances.

    GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormat, GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormatArgs

    DataFormatUnspecified
    DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
    Jsonl
    JSONLExamples are stored in JSONL files.
    GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormatDataFormatUnspecified
    DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
    GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormatJsonl
    JSONLExamples are stored in JSONL files.
    DataFormatUnspecified
    DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
    Jsonl
    JSONLExamples are stored in JSONL files.
    DataFormatUnspecified
    DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
    Jsonl
    JSONLExamples are stored in JSONL files.
    DATA_FORMAT_UNSPECIFIED
    DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
    JSONL
    JSONLExamples are stored in JSONL files.
    "DATA_FORMAT_UNSPECIFIED"
    DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
    "JSONL"
    JSONLExamples are stored in JSONL files.

    GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse, GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponseArgs

    DataFormat string
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    GcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage location for the input instances.
    DataFormat string
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    GcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage location for the input instances.
    dataFormat String
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage location for the input instances.
    dataFormat string
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage location for the input instances.
    data_format str
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcs_source GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage location for the input instances.
    dataFormat String
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcsSource Property Map
    The Cloud Storage location for the input instances.

    GoogleCloudAiplatformV1beta1ExamplesResponse, GoogleCloudAiplatformV1beta1ExamplesResponseArgs

    ExampleGcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse
    The Cloud Storage input instances.
    GcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
    NearestNeighborSearchConfig object
    The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
    NeighborCount int
    The number of neighbors to return when querying for examples.
    Presets Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PresetsResponse
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    ExampleGcsSource GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse
    The Cloud Storage input instances.
    GcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
    NearestNeighborSearchConfig interface{}
    The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
    NeighborCount int
    The number of neighbors to return when querying for examples.
    Presets GoogleCloudAiplatformV1beta1PresetsResponse
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    exampleGcsSource GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse
    The Cloud Storage input instances.
    gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
    nearestNeighborSearchConfig Object
    The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
    neighborCount Integer
    The number of neighbors to return when querying for examples.
    presets GoogleCloudAiplatformV1beta1PresetsResponse
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    exampleGcsSource GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse
    The Cloud Storage input instances.
    gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
    nearestNeighborSearchConfig any
    The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
    neighborCount number
    The number of neighbors to return when querying for examples.
    presets GoogleCloudAiplatformV1beta1PresetsResponse
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    example_gcs_source GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse
    The Cloud Storage input instances.
    gcs_source GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
    nearest_neighbor_search_config Any
    The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
    neighbor_count int
    The number of neighbors to return when querying for examples.
    presets GoogleCloudAiplatformV1beta1PresetsResponse
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    exampleGcsSource Property Map
    The Cloud Storage input instances.
    gcsSource Property Map
    The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
    nearestNeighborSearchConfig Any
    The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
    neighborCount Number
    The number of neighbors to return when querying for examples.
    presets Property Map
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

    GoogleCloudAiplatformV1beta1ExplanationMetadata, GoogleCloudAiplatformV1beta1ExplanationMetadataArgs

    Inputs Dictionary<string, string>
    Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
    Outputs Dictionary<string, string>
    Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
    FeatureAttributionsSchemaUri string
    Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    LatentSpaceSource string
    Name of the source to generate embeddings for example based explanations.
    Inputs map[string]string
    Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
    Outputs map[string]string
    Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
    FeatureAttributionsSchemaUri string
    Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    LatentSpaceSource string
    Name of the source to generate embeddings for example based explanations.
    inputs Map<String,String>
    Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
    outputs Map<String,String>
    Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
    featureAttributionsSchemaUri String
    Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    latentSpaceSource String
    Name of the source to generate embeddings for example based explanations.
    inputs {[key: string]: string}
    Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
    outputs {[key: string]: string}
    Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
    featureAttributionsSchemaUri string
    Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    latentSpaceSource string
    Name of the source to generate embeddings for example based explanations.
    inputs Mapping[str, str]
    Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
    outputs Mapping[str, str]
    Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
    feature_attributions_schema_uri str
    Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    latent_space_source str
    Name of the source to generate embeddings for example based explanations.
    inputs Map<String>
    Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
    outputs Map<String>
    Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
    featureAttributionsSchemaUri String
    Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    latentSpaceSource String
    Name of the source to generate embeddings for example based explanations.

    GoogleCloudAiplatformV1beta1ExplanationMetadataResponse, GoogleCloudAiplatformV1beta1ExplanationMetadataResponseArgs

    FeatureAttributionsSchemaUri string
    Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    Inputs Dictionary<string, string>
    Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
    LatentSpaceSource string
    Name of the source to generate embeddings for example based explanations.
    Outputs Dictionary<string, string>
    Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
    FeatureAttributionsSchemaUri string
    Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    Inputs map[string]string
    Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
    LatentSpaceSource string
    Name of the source to generate embeddings for example based explanations.
    Outputs map[string]string
    Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
    featureAttributionsSchemaUri String
    Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    inputs Map<String,String>
    Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
    latentSpaceSource String
    Name of the source to generate embeddings for example based explanations.
    outputs Map<String,String>
    Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
    featureAttributionsSchemaUri string
    Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    inputs {[key: string]: string}
    Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
    latentSpaceSource string
    Name of the source to generate embeddings for example based explanations.
    outputs {[key: string]: string}
    Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
    feature_attributions_schema_uri str
    Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    inputs Mapping[str, str]
    Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
    latent_space_source str
    Name of the source to generate embeddings for example based explanations.
    outputs Mapping[str, str]
    Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
    featureAttributionsSchemaUri String
    Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Vertex AI. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    inputs Map<String>
    Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
    latentSpaceSource String
    Name of the source to generate embeddings for example based explanations.
    outputs Map<String>
    Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.

    GoogleCloudAiplatformV1beta1ExplanationParameters, GoogleCloudAiplatformV1beta1ExplanationParametersArgs

    Examples Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1Examples
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    IntegratedGradientsAttribution Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1IntegratedGradientsAttribution
    An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
    OutputIndices List<object>
    If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
    SampledShapleyAttribution Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SampledShapleyAttribution
    An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
    TopK int
    If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
    XraiAttribution Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1XraiAttribution
    An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
    Examples GoogleCloudAiplatformV1beta1Examples
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    IntegratedGradientsAttribution GoogleCloudAiplatformV1beta1IntegratedGradientsAttribution
    An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
    OutputIndices []interface{}
    If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
    SampledShapleyAttribution GoogleCloudAiplatformV1beta1SampledShapleyAttribution
    An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
    TopK int
    If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
    XraiAttribution GoogleCloudAiplatformV1beta1XraiAttribution
    An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
    examples GoogleCloudAiplatformV1beta1Examples
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    integratedGradientsAttribution GoogleCloudAiplatformV1beta1IntegratedGradientsAttribution
    An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
    outputIndices List<Object>
    If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
    sampledShapleyAttribution GoogleCloudAiplatformV1beta1SampledShapleyAttribution
    An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
    topK Integer
    If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
    xraiAttribution GoogleCloudAiplatformV1beta1XraiAttribution
    An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
    examples GoogleCloudAiplatformV1beta1Examples
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    integratedGradientsAttribution GoogleCloudAiplatformV1beta1IntegratedGradientsAttribution
    An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
    outputIndices any[]
    If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
    sampledShapleyAttribution GoogleCloudAiplatformV1beta1SampledShapleyAttribution
    An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
    topK number
    If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
    xraiAttribution GoogleCloudAiplatformV1beta1XraiAttribution
    An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
    examples GoogleCloudAiplatformV1beta1Examples
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    integrated_gradients_attribution GoogleCloudAiplatformV1beta1IntegratedGradientsAttribution
    An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
    output_indices Sequence[Any]
    If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
    sampled_shapley_attribution GoogleCloudAiplatformV1beta1SampledShapleyAttribution
    An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
    top_k int
    If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
    xrai_attribution GoogleCloudAiplatformV1beta1XraiAttribution
    An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
    examples Property Map
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    integratedGradientsAttribution Property Map
    An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
    outputIndices List<Any>
    If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
    sampledShapleyAttribution Property Map
    An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
    topK Number
    If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
    xraiAttribution Property Map
    An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.

    GoogleCloudAiplatformV1beta1ExplanationParametersResponse, GoogleCloudAiplatformV1beta1ExplanationParametersResponseArgs

    Examples Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExamplesResponse
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    IntegratedGradientsAttribution Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse
    An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
    OutputIndices List<object>
    If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
    SampledShapleyAttribution Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse
    An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
    TopK int
    If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
    XraiAttribution Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1XraiAttributionResponse
    An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
    Examples GoogleCloudAiplatformV1beta1ExamplesResponse
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    IntegratedGradientsAttribution GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse
    An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
    OutputIndices []interface{}
    If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
    SampledShapleyAttribution GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse
    An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
    TopK int
    If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
    XraiAttribution GoogleCloudAiplatformV1beta1XraiAttributionResponse
    An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
    examples GoogleCloudAiplatformV1beta1ExamplesResponse
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    integratedGradientsAttribution GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse
    An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
    outputIndices List<Object>
    If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
    sampledShapleyAttribution GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse
    An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
    topK Integer
    If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
    xraiAttribution GoogleCloudAiplatformV1beta1XraiAttributionResponse
    An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
    examples GoogleCloudAiplatformV1beta1ExamplesResponse
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    integratedGradientsAttribution GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse
    An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
    outputIndices any[]
    If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
    sampledShapleyAttribution GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse
    An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
    topK number
    If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
    xraiAttribution GoogleCloudAiplatformV1beta1XraiAttributionResponse
    An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
    examples GoogleCloudAiplatformV1beta1ExamplesResponse
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    integrated_gradients_attribution GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse
    An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
    output_indices Sequence[Any]
    If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
    sampled_shapley_attribution GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse
    An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
    top_k int
    If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
    xrai_attribution GoogleCloudAiplatformV1beta1XraiAttributionResponse
    An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
    examples Property Map
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    integratedGradientsAttribution Property Map
    An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
    outputIndices List<Any>
    If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
    sampledShapleyAttribution Property Map
    An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
    topK Number
    If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
    xraiAttribution Property Map
    An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.

    GoogleCloudAiplatformV1beta1ExplanationSpec, GoogleCloudAiplatformV1beta1ExplanationSpecArgs

    Parameters Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationParameters
    Parameters that configure explaining of the Model's predictions.
    Metadata Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationMetadata
    Optional. Metadata describing the Model's input and output for explanation.
    Parameters GoogleCloudAiplatformV1beta1ExplanationParameters
    Parameters that configure explaining of the Model's predictions.
    Metadata GoogleCloudAiplatformV1beta1ExplanationMetadata
    Optional. Metadata describing the Model's input and output for explanation.
    parameters GoogleCloudAiplatformV1beta1ExplanationParameters
    Parameters that configure explaining of the Model's predictions.
    metadata GoogleCloudAiplatformV1beta1ExplanationMetadata
    Optional. Metadata describing the Model's input and output for explanation.
    parameters GoogleCloudAiplatformV1beta1ExplanationParameters
    Parameters that configure explaining of the Model's predictions.
    metadata GoogleCloudAiplatformV1beta1ExplanationMetadata
    Optional. Metadata describing the Model's input and output for explanation.
    parameters GoogleCloudAiplatformV1beta1ExplanationParameters
    Parameters that configure explaining of the Model's predictions.
    metadata GoogleCloudAiplatformV1beta1ExplanationMetadata
    Optional. Metadata describing the Model's input and output for explanation.
    parameters Property Map
    Parameters that configure explaining of the Model's predictions.
    metadata Property Map
    Optional. Metadata describing the Model's input and output for explanation.

    GoogleCloudAiplatformV1beta1ExplanationSpecResponse, GoogleCloudAiplatformV1beta1ExplanationSpecResponseArgs

    Metadata GoogleCloudAiplatformV1beta1ExplanationMetadataResponse
    Optional. Metadata describing the Model's input and output for explanation.
    Parameters GoogleCloudAiplatformV1beta1ExplanationParametersResponse
    Parameters that configure explaining of the Model's predictions.
    metadata GoogleCloudAiplatformV1beta1ExplanationMetadataResponse
    Optional. Metadata describing the Model's input and output for explanation.
    parameters GoogleCloudAiplatformV1beta1ExplanationParametersResponse
    Parameters that configure explaining of the Model's predictions.
    metadata GoogleCloudAiplatformV1beta1ExplanationMetadataResponse
    Optional. Metadata describing the Model's input and output for explanation.
    parameters GoogleCloudAiplatformV1beta1ExplanationParametersResponse
    Parameters that configure explaining of the Model's predictions.
    metadata GoogleCloudAiplatformV1beta1ExplanationMetadataResponse
    Optional. Metadata describing the Model's input and output for explanation.
    parameters GoogleCloudAiplatformV1beta1ExplanationParametersResponse
    Parameters that configure explaining of the Model's predictions.
    metadata Property Map
    Optional. Metadata describing the Model's input and output for explanation.
    parameters Property Map
    Parameters that configure explaining of the Model's predictions.

    GoogleCloudAiplatformV1beta1FeatureNoiseSigma, GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs

    NoiseSigma []GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeature
    Noise sigma per feature. No noise is added to features that are not set.
    noiseSigma List<GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeature>
    Noise sigma per feature. No noise is added to features that are not set.
    noiseSigma GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeature[]
    Noise sigma per feature. No noise is added to features that are not set.
    noise_sigma Sequence[GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeature]
    Noise sigma per feature. No noise is added to features that are not set.
    noiseSigma List<Property Map>
    Noise sigma per feature. No noise is added to features that are not set.

    GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeature, GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs

    Name string
    The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
    Sigma double
    This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
    Name string
    The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
    Sigma float64
    This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
    name String
    The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
    sigma Double
    This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
    name string
    The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
    sigma number
    This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
    name str
    The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
    sigma float
    This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
    name String
    The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
    sigma Number
    This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.

    GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse, GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponseArgs

    Name string
    The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
    Sigma double
    This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
    Name string
    The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
    Sigma float64
    This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
    name String
    The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
    sigma Double
    This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
    name string
    The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
    sigma number
    This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
    name str
    The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
    sigma float
    This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
    name String
    The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
    sigma Number
    This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.

    GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse, GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponseArgs

    NoiseSigma []GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse
    Noise sigma per feature. No noise is added to features that are not set.
    noiseSigma List<GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse>
    Noise sigma per feature. No noise is added to features that are not set.
    noiseSigma GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse[]
    Noise sigma per feature. No noise is added to features that are not set.
    noise_sigma Sequence[GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse]
    Noise sigma per feature. No noise is added to features that are not set.
    noiseSigma List<Property Map>
    Noise sigma per feature. No noise is added to features that are not set.

    GoogleCloudAiplatformV1beta1FeatureStatsAnomaly, GoogleCloudAiplatformV1beta1FeatureStatsAnomalyArgs

    AnomalyDetectionThreshold double
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    AnomalyUri string
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    DistributionDeviation double
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    EndTime string
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    Score double
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    StartTime string
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    StatsUri string
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    AnomalyDetectionThreshold float64
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    AnomalyUri string
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    DistributionDeviation float64
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    EndTime string
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    Score float64
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    StartTime string
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    StatsUri string
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    anomalyDetectionThreshold Double
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    anomalyUri String
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    distributionDeviation Double
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    endTime String
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    score Double
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    startTime String
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    statsUri String
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    anomalyDetectionThreshold number
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    anomalyUri string
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    distributionDeviation number
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    endTime string
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    score number
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    startTime string
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    statsUri string
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    anomaly_detection_threshold float
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    anomaly_uri str
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    distribution_deviation float
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    end_time str
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    score float
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    start_time str
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    stats_uri str
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    anomalyDetectionThreshold Number
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    anomalyUri String
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    distributionDeviation Number
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    endTime String
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    score Number
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    startTime String
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    statsUri String
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.

    GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse, GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponseArgs

    AnomalyDetectionThreshold double
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    AnomalyUri string
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    DistributionDeviation double
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    EndTime string
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    Score double
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    StartTime string
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    StatsUri string
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    AnomalyDetectionThreshold float64
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    AnomalyUri string
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    DistributionDeviation float64
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    EndTime string
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    Score float64
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    StartTime string
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    StatsUri string
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    anomalyDetectionThreshold Double
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    anomalyUri String
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    distributionDeviation Double
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    endTime String
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    score Double
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    startTime String
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    statsUri String
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    anomalyDetectionThreshold number
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    anomalyUri string
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    distributionDeviation number
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    endTime string
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    score number
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    startTime string
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    statsUri string
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    anomaly_detection_threshold float
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    anomaly_uri str
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    distribution_deviation float
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    end_time str
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    score float
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    start_time str
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    stats_uri str
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
    anomalyDetectionThreshold Number
    This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
    anomalyUri String
    Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message [tensorflow.metadata.v0.AnomalyInfo] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
    distributionDeviation Number
    Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
    endTime String
    The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).
    score Number
    Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
    startTime String
    The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
    statsUri String
    Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.

    GoogleCloudAiplatformV1beta1GcsDestination, GoogleCloudAiplatformV1beta1GcsDestinationArgs

    OutputUriPrefix string
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    OutputUriPrefix string
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    outputUriPrefix String
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    outputUriPrefix string
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    output_uri_prefix str
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    outputUriPrefix String
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

    GoogleCloudAiplatformV1beta1GcsDestinationResponse, GoogleCloudAiplatformV1beta1GcsDestinationResponseArgs

    OutputUriPrefix string
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    OutputUriPrefix string
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    outputUriPrefix String
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    outputUriPrefix string
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    output_uri_prefix str
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
    outputUriPrefix String
    Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

    GoogleCloudAiplatformV1beta1GcsSource, GoogleCloudAiplatformV1beta1GcsSourceArgs

    Uris List<string>
    Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
    Uris []string
    Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
    uris List<String>
    Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
    uris string[]
    Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
    uris Sequence[str]
    Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
    uris List<String>
    Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.

    GoogleCloudAiplatformV1beta1GcsSourceResponse, GoogleCloudAiplatformV1beta1GcsSourceResponseArgs

    Uris List<string>
    Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
    Uris []string
    Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
    uris List<String>
    Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
    uris string[]
    Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
    uris Sequence[str]
    Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
    uris List<String>
    Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.

    GoogleCloudAiplatformV1beta1IntegratedGradientsAttribution, GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionArgs

    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    BlurBaselineConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BlurBaselineConfig
    Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    SmoothGradConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    BlurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfig
    Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    SmoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Integer
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfig
    Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfig
    Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    step_count int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blur_baseline_config GoogleCloudAiplatformV1beta1BlurBaselineConfig
    Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smooth_grad_config GoogleCloudAiplatformV1beta1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig Property Map
    Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig Property Map
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

    GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse, GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponseArgs

    BlurBaselineConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse
    Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    SmoothGradConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    BlurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse
    Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    SmoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse
    Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Integer
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse
    Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blur_baseline_config GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse
    Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smooth_grad_config GoogleCloudAiplatformV1beta1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    step_count int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig Property Map
    Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig Property Map
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.

    GoogleCloudAiplatformV1beta1MachineSpec, GoogleCloudAiplatformV1beta1MachineSpecArgs

    AcceleratorCount int
    The number of accelerators to attach to the machine.
    AcceleratorType Pulumi.GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    MachineType string
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    TpuTopology string
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    AcceleratorCount int
    The number of accelerators to attach to the machine.
    AcceleratorType GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    MachineType string
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    TpuTopology string
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    acceleratorCount Integer
    The number of accelerators to attach to the machine.
    acceleratorType GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    machineType String
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    tpuTopology String
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    acceleratorCount number
    The number of accelerators to attach to the machine.
    acceleratorType GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    machineType string
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    tpuTopology string
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    accelerator_count int
    The number of accelerators to attach to the machine.
    accelerator_type GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    machine_type str
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    tpu_topology str
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    acceleratorCount Number
    The number of accelerators to attach to the machine.
    acceleratorType "ACCELERATOR_TYPE_UNSPECIFIED" | "NVIDIA_TESLA_K80" | "NVIDIA_TESLA_P100" | "NVIDIA_TESLA_V100" | "NVIDIA_TESLA_P4" | "NVIDIA_TESLA_T4" | "NVIDIA_TESLA_A100" | "NVIDIA_A100_80GB" | "NVIDIA_L4" | "NVIDIA_H100_80GB" | "TPU_V2" | "TPU_V3" | "TPU_V4_POD" | "TPU_V5_LITEPOD"
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    machineType String
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    tpuTopology String
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorType, GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeArgs

    AcceleratorTypeUnspecified
    ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
    NvidiaTeslaK80
    NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
    NvidiaTeslaP100
    NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
    NvidiaTeslaV100
    NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
    NvidiaTeslaP4
    NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
    NvidiaTeslaT4
    NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
    NvidiaTeslaA100
    NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
    NvidiaA10080gb
    NVIDIA_A100_80GBNvidia A100 80GB GPU.
    NvidiaL4
    NVIDIA_L4Nvidia L4 GPU.
    NvidiaH10080gb
    NVIDIA_H100_80GBNvidia H100 80Gb GPU.
    TpuV2
    TPU_V2TPU v2.
    TpuV3
    TPU_V3TPU v3.
    TpuV4Pod
    TPU_V4_PODTPU v4.
    TpuV5Litepod
    TPU_V5_LITEPODTPU v5.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeAcceleratorTypeUnspecified
    ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaTeslaK80
    NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaTeslaP100
    NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaTeslaV100
    NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaTeslaP4
    NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaTeslaT4
    NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaTeslaA100
    NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaA10080gb
    NVIDIA_A100_80GBNvidia A100 80GB GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaL4
    NVIDIA_L4Nvidia L4 GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeNvidiaH10080gb
    NVIDIA_H100_80GBNvidia H100 80Gb GPU.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeTpuV2
    TPU_V2TPU v2.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeTpuV3
    TPU_V3TPU v3.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeTpuV4Pod
    TPU_V4_PODTPU v4.
    GoogleCloudAiplatformV1beta1MachineSpecAcceleratorTypeTpuV5Litepod
    TPU_V5_LITEPODTPU v5.
    AcceleratorTypeUnspecified
    ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
    NvidiaTeslaK80
    NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
    NvidiaTeslaP100
    NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
    NvidiaTeslaV100
    NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
    NvidiaTeslaP4
    NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
    NvidiaTeslaT4
    NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
    NvidiaTeslaA100
    NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
    NvidiaA10080gb
    NVIDIA_A100_80GBNvidia A100 80GB GPU.
    NvidiaL4
    NVIDIA_L4Nvidia L4 GPU.
    NvidiaH10080gb
    NVIDIA_H100_80GBNvidia H100 80Gb GPU.
    TpuV2
    TPU_V2TPU v2.
    TpuV3
    TPU_V3TPU v3.
    TpuV4Pod
    TPU_V4_PODTPU v4.
    TpuV5Litepod
    TPU_V5_LITEPODTPU v5.
    AcceleratorTypeUnspecified
    ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
    NvidiaTeslaK80
    NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
    NvidiaTeslaP100
    NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
    NvidiaTeslaV100
    NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
    NvidiaTeslaP4
    NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
    NvidiaTeslaT4
    NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
    NvidiaTeslaA100
    NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
    NvidiaA10080gb
    NVIDIA_A100_80GBNvidia A100 80GB GPU.
    NvidiaL4
    NVIDIA_L4Nvidia L4 GPU.
    NvidiaH10080gb
    NVIDIA_H100_80GBNvidia H100 80Gb GPU.
    TpuV2
    TPU_V2TPU v2.
    TpuV3
    TPU_V3TPU v3.
    TpuV4Pod
    TPU_V4_PODTPU v4.
    TpuV5Litepod
    TPU_V5_LITEPODTPU v5.
    ACCELERATOR_TYPE_UNSPECIFIED
    ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
    NVIDIA_TESLA_K80
    NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
    NVIDIA_TESLA_P100
    NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
    NVIDIA_TESLA_V100
    NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
    NVIDIA_TESLA_P4
    NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
    NVIDIA_TESLA_T4
    NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
    NVIDIA_TESLA_A100
    NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
    NVIDIA_A10080GB
    NVIDIA_A100_80GBNvidia A100 80GB GPU.
    NVIDIA_L4
    NVIDIA_L4Nvidia L4 GPU.
    NVIDIA_H10080GB
    NVIDIA_H100_80GBNvidia H100 80Gb GPU.
    TPU_V2
    TPU_V2TPU v2.
    TPU_V3
    TPU_V3TPU v3.
    TPU_V4_POD
    TPU_V4_PODTPU v4.
    TPU_V5_LITEPOD
    TPU_V5_LITEPODTPU v5.
    "ACCELERATOR_TYPE_UNSPECIFIED"
    ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
    "NVIDIA_TESLA_K80"
    NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
    "NVIDIA_TESLA_P100"
    NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
    "NVIDIA_TESLA_V100"
    NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
    "NVIDIA_TESLA_P4"
    NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
    "NVIDIA_TESLA_T4"
    NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
    "NVIDIA_TESLA_A100"
    NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
    "NVIDIA_A100_80GB"
    NVIDIA_A100_80GBNvidia A100 80GB GPU.
    "NVIDIA_L4"
    NVIDIA_L4Nvidia L4 GPU.
    "NVIDIA_H100_80GB"
    NVIDIA_H100_80GBNvidia H100 80Gb GPU.
    "TPU_V2"
    TPU_V2TPU v2.
    "TPU_V3"
    TPU_V3TPU v3.
    "TPU_V4_POD"
    TPU_V4_PODTPU v4.
    "TPU_V5_LITEPOD"
    TPU_V5_LITEPODTPU v5.

    GoogleCloudAiplatformV1beta1MachineSpecResponse, GoogleCloudAiplatformV1beta1MachineSpecResponseArgs

    AcceleratorCount int
    The number of accelerators to attach to the machine.
    AcceleratorType string
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    MachineType string
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    TpuTopology string
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    AcceleratorCount int
    The number of accelerators to attach to the machine.
    AcceleratorType string
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    MachineType string
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    TpuTopology string
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    acceleratorCount Integer
    The number of accelerators to attach to the machine.
    acceleratorType String
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    machineType String
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    tpuTopology String
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    acceleratorCount number
    The number of accelerators to attach to the machine.
    acceleratorType string
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    machineType string
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    tpuTopology string
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    accelerator_count int
    The number of accelerators to attach to the machine.
    accelerator_type str
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    machine_type str
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    tpu_topology str
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
    acceleratorCount Number
    The number of accelerators to attach to the machine.
    acceleratorType String
    Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
    machineType String
    Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
    tpuTopology String
    Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

    GoogleCloudAiplatformV1beta1ManualBatchTuningParameters, GoogleCloudAiplatformV1beta1ManualBatchTuningParametersArgs

    BatchSize int
    Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
    BatchSize int
    Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
    batchSize Integer
    Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
    batchSize number
    Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
    batch_size int
    Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
    batchSize Number
    Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.

    GoogleCloudAiplatformV1beta1ManualBatchTuningParametersResponse, GoogleCloudAiplatformV1beta1ManualBatchTuningParametersResponseArgs

    BatchSize int
    Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
    BatchSize int
    Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
    batchSize Integer
    Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
    batchSize number
    Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
    batch_size int
    Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.
    batchSize Number
    Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.

    GoogleCloudAiplatformV1beta1ModelContainerSpec, GoogleCloudAiplatformV1beta1ModelContainerSpecArgs

    ImageUri string
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    Args List<string>
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    Command List<string>
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    DeploymentTimeout string
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    Env List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EnvVar>
    Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
    HealthProbe Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1Probe
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    HealthRoute string
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    Ports List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1Port>
    Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
    PredictRoute string
    Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    SharedMemorySizeMb string
    Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
    StartupProbe Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1Probe
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    ImageUri string
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    Args []string
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    Command []string
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    DeploymentTimeout string
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    Env []GoogleCloudAiplatformV1beta1EnvVar
    Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
    HealthProbe GoogleCloudAiplatformV1beta1Probe
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    HealthRoute string
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    Ports []GoogleCloudAiplatformV1beta1Port
    Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
    PredictRoute string
    Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    SharedMemorySizeMb string
    Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
    StartupProbe GoogleCloudAiplatformV1beta1Probe
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    imageUri String
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    args List<String>
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command List<String>
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deploymentTimeout String
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env List<GoogleCloudAiplatformV1beta1EnvVar>
    Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
    healthProbe GoogleCloudAiplatformV1beta1Probe
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    healthRoute String
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    ports List<GoogleCloudAiplatformV1beta1Port>
    Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
    predictRoute String
    Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    sharedMemorySizeMb String
    Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
    startupProbe GoogleCloudAiplatformV1beta1Probe
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    imageUri string
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    args string[]
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command string[]
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deploymentTimeout string
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env GoogleCloudAiplatformV1beta1EnvVar[]
    Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
    healthProbe GoogleCloudAiplatformV1beta1Probe
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    healthRoute string
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    ports GoogleCloudAiplatformV1beta1Port[]
    Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
    predictRoute string
    Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    sharedMemorySizeMb string
    Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
    startupProbe GoogleCloudAiplatformV1beta1Probe
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    image_uri str
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    args Sequence[str]
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command Sequence[str]
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deployment_timeout str
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env Sequence[GoogleCloudAiplatformV1beta1EnvVar]
    Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
    health_probe GoogleCloudAiplatformV1beta1Probe
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    health_route str
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    ports Sequence[GoogleCloudAiplatformV1beta1Port]
    Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
    predict_route str
    Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    shared_memory_size_mb str
    Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
    startup_probe GoogleCloudAiplatformV1beta1Probe
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    imageUri String
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    args List<String>
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command List<String>
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deploymentTimeout String
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env List<Property Map>
    Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
    healthProbe Property Map
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    healthRoute String
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    ports List<Property Map>
    Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
    predictRoute String
    Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    sharedMemorySizeMb String
    Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
    startupProbe Property Map
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.

    GoogleCloudAiplatformV1beta1ModelContainerSpecResponse, GoogleCloudAiplatformV1beta1ModelContainerSpecResponseArgs

    Args List<string>
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    Command List<string>
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    DeploymentTimeout string
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    Env List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EnvVarResponse>
    Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
    HealthProbe Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeResponse
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    HealthRoute string
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    ImageUri string
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    Ports List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PortResponse>
    Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
    PredictRoute string
    Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    SharedMemorySizeMb string
    Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
    StartupProbe Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeResponse
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    Args []string
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    Command []string
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    DeploymentTimeout string
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    Env []GoogleCloudAiplatformV1beta1EnvVarResponse
    Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
    HealthProbe GoogleCloudAiplatformV1beta1ProbeResponse
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    HealthRoute string
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    ImageUri string
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    Ports []GoogleCloudAiplatformV1beta1PortResponse
    Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
    PredictRoute string
    Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    SharedMemorySizeMb string
    Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
    StartupProbe GoogleCloudAiplatformV1beta1ProbeResponse
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    args List<String>
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command List<String>
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deploymentTimeout String
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env List<GoogleCloudAiplatformV1beta1EnvVarResponse>
    Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
    healthProbe GoogleCloudAiplatformV1beta1ProbeResponse
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    healthRoute String
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    imageUri String
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    ports List<GoogleCloudAiplatformV1beta1PortResponse>
    Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
    predictRoute String
    Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    sharedMemorySizeMb String
    Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
    startupProbe GoogleCloudAiplatformV1beta1ProbeResponse
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    args string[]
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command string[]
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deploymentTimeout string
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env GoogleCloudAiplatformV1beta1EnvVarResponse[]
    Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
    healthProbe GoogleCloudAiplatformV1beta1ProbeResponse
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    healthRoute string
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    imageUri string
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    ports GoogleCloudAiplatformV1beta1PortResponse[]
    Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
    predictRoute string
    Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    sharedMemorySizeMb string
    Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
    startupProbe GoogleCloudAiplatformV1beta1ProbeResponse
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    args Sequence[str]
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command Sequence[str]
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deployment_timeout str
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env Sequence[GoogleCloudAiplatformV1beta1EnvVarResponse]
    Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
    health_probe GoogleCloudAiplatformV1beta1ProbeResponse
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    health_route str
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    image_uri str
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    ports Sequence[GoogleCloudAiplatformV1beta1PortResponse]
    Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
    predict_route str
    Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    shared_memory_size_mb str
    Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
    startup_probe GoogleCloudAiplatformV1beta1ProbeResponse
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    args List<String>
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command List<String>
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deploymentTimeout String
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env List<Property Map>
    Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
    healthProbe Property Map
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    healthRoute String
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    imageUri String
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    ports List<Property Map>
    Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
    predictRoute String
    Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    sharedMemorySizeMb String
    Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
    startupProbe Property Map
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.

    GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfig, GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigArgs

    EmailAlertConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfig
    Email alert config.
    EnableLogging bool
    Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    NotificationChannels List<string>
    Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
    EmailAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfig
    Email alert config.
    EnableLogging bool
    Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    NotificationChannels []string
    Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
    emailAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfig
    Email alert config.
    enableLogging Boolean
    Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    notificationChannels List<String>
    Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
    emailAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfig
    Email alert config.
    enableLogging boolean
    Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    notificationChannels string[]
    Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
    email_alert_config GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfig
    Email alert config.
    enable_logging bool
    Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    notification_channels Sequence[str]
    Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
    emailAlertConfig Property Map
    Email alert config.
    enableLogging Boolean
    Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    notificationChannels List<String>
    Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/

    GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfig, GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigArgs

    UserEmails List<string>
    The email addresses to send the alert.
    UserEmails []string
    The email addresses to send the alert.
    userEmails List<String>
    The email addresses to send the alert.
    userEmails string[]
    The email addresses to send the alert.
    user_emails Sequence[str]
    The email addresses to send the alert.
    userEmails List<String>
    The email addresses to send the alert.

    GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse, GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponseArgs

    UserEmails List<string>
    The email addresses to send the alert.
    UserEmails []string
    The email addresses to send the alert.
    userEmails List<String>
    The email addresses to send the alert.
    userEmails string[]
    The email addresses to send the alert.
    user_emails Sequence[str]
    The email addresses to send the alert.
    userEmails List<String>
    The email addresses to send the alert.

    GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse, GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponseArgs

    EmailAlertConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse
    Email alert config.
    EnableLogging bool
    Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    NotificationChannels List<string>
    Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
    EmailAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse
    Email alert config.
    EnableLogging bool
    Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    NotificationChannels []string
    Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
    emailAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse
    Email alert config.
    enableLogging Boolean
    Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    notificationChannels List<String>
    Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
    emailAlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse
    Email alert config.
    enableLogging boolean
    Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    notificationChannels string[]
    Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
    email_alert_config GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigEmailAlertConfigResponse
    Email alert config.
    enable_logging bool
    Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    notification_channels Sequence[str]
    Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/
    emailAlertConfig Property Map
    Email alert config.
    enableLogging Boolean
    Dump the anomalies to Cloud Logging. The anomalies will be put to json payload encoded from proto google.cloud.aiplatform.logging.ModelMonitoringAnomaliesLogEntry. This can be further sinked to Pub/Sub or any other services supported by Cloud Logging.
    notificationChannels List<String>
    Resource names of the NotificationChannels to send alert. Must be of the format projects//notificationChannels/

    GoogleCloudAiplatformV1beta1ModelMonitoringConfig, GoogleCloudAiplatformV1beta1ModelMonitoringConfigArgs

    AlertConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfig
    Model monitoring alert config.
    AnalysisInstanceSchemaUri string
    YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
    ObjectiveConfigs List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig>
    Model monitoring objective config.
    StatsAnomaliesBaseDirectory Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestination
    A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.
    AlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfig
    Model monitoring alert config.
    AnalysisInstanceSchemaUri string
    YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
    ObjectiveConfigs []GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig
    Model monitoring objective config.
    StatsAnomaliesBaseDirectory GoogleCloudAiplatformV1beta1GcsDestination
    A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.
    alertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfig
    Model monitoring alert config.
    analysisInstanceSchemaUri String
    YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
    objectiveConfigs List<GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig>
    Model monitoring objective config.
    statsAnomaliesBaseDirectory GoogleCloudAiplatformV1beta1GcsDestination
    A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.
    alertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfig
    Model monitoring alert config.
    analysisInstanceSchemaUri string
    YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
    objectiveConfigs GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig[]
    Model monitoring objective config.
    statsAnomaliesBaseDirectory GoogleCloudAiplatformV1beta1GcsDestination
    A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.
    alert_config GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfig
    Model monitoring alert config.
    analysis_instance_schema_uri str
    YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
    objective_configs Sequence[GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig]
    Model monitoring objective config.
    stats_anomalies_base_directory GoogleCloudAiplatformV1beta1GcsDestination
    A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.
    alertConfig Property Map
    Model monitoring alert config.
    analysisInstanceSchemaUri String
    YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
    objectiveConfigs List<Property Map>
    Model monitoring objective config.
    statsAnomaliesBaseDirectory Property Map
    A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.

    GoogleCloudAiplatformV1beta1ModelMonitoringConfigResponse, GoogleCloudAiplatformV1beta1ModelMonitoringConfigResponseArgs

    AlertConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse
    Model monitoring alert config.
    AnalysisInstanceSchemaUri string
    YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
    ObjectiveConfigs List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse>
    Model monitoring objective config.
    StatsAnomaliesBaseDirectory Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationResponse
    A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.
    AlertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse
    Model monitoring alert config.
    AnalysisInstanceSchemaUri string
    YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
    ObjectiveConfigs []GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse
    Model monitoring objective config.
    StatsAnomaliesBaseDirectory GoogleCloudAiplatformV1beta1GcsDestinationResponse
    A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.
    alertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse
    Model monitoring alert config.
    analysisInstanceSchemaUri String
    YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
    objectiveConfigs List<GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse>
    Model monitoring objective config.
    statsAnomaliesBaseDirectory GoogleCloudAiplatformV1beta1GcsDestinationResponse
    A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.
    alertConfig GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse
    Model monitoring alert config.
    analysisInstanceSchemaUri string
    YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
    objectiveConfigs GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse[]
    Model monitoring objective config.
    statsAnomaliesBaseDirectory GoogleCloudAiplatformV1beta1GcsDestinationResponse
    A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.
    alert_config GoogleCloudAiplatformV1beta1ModelMonitoringAlertConfigResponse
    Model monitoring alert config.
    analysis_instance_schema_uri str
    YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
    objective_configs Sequence[GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse]
    Model monitoring objective config.
    stats_anomalies_base_directory GoogleCloudAiplatformV1beta1GcsDestinationResponse
    A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.
    alertConfig Property Map
    Model monitoring alert config.
    analysisInstanceSchemaUri String
    YAML schema file uri in Cloud Storage describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.
    objectiveConfigs List<Property Map>
    Model monitoring objective config.
    statsAnomaliesBaseDirectory Property Map
    A Google Cloud Storage location for batch prediction model monitoring to dump statistics and anomalies. If not provided, a folder will be created in customer project to hold statistics and anomalies.

    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfig, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigArgs

    ExplanationConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfig
    The config for integrating with Vertex Explainable AI.
    PredictionDriftDetectionConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfig
    The config for drift of prediction data.
    TrainingDataset Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDataset
    Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
    TrainingPredictionSkewDetectionConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfig
    The config for skew between training data and prediction data.
    explanationConfig Property Map
    The config for integrating with Vertex Explainable AI.
    predictionDriftDetectionConfig Property Map
    The config for drift of prediction data.
    trainingDataset Property Map
    Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
    trainingPredictionSkewDetectionConfig Property Map
    The config for skew between training data and prediction data.

    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfig, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigArgs

    EnableFeatureAttributes bool
    If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
    ExplanationBaseline Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline
    Predictions generated by the BatchPredictionJob using baseline dataset.
    EnableFeatureAttributes bool
    If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
    ExplanationBaseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline
    Predictions generated by the BatchPredictionJob using baseline dataset.
    enableFeatureAttributes Boolean
    If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
    explanationBaseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline
    Predictions generated by the BatchPredictionJob using baseline dataset.
    enableFeatureAttributes boolean
    If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
    explanationBaseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline
    Predictions generated by the BatchPredictionJob using baseline dataset.
    enable_feature_attributes bool
    If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
    explanation_baseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline
    Predictions generated by the BatchPredictionJob using baseline dataset.
    enableFeatureAttributes Boolean
    If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
    explanationBaseline Property Map
    Predictions generated by the BatchPredictionJob using baseline dataset.

    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaseline, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineArgs

    Bigquery GoogleCloudAiplatformV1beta1BigQueryDestination
    BigQuery location for BatchExplain output.
    Gcs GoogleCloudAiplatformV1beta1GcsDestination
    Cloud Storage location for BatchExplain output.
    PredictionFormat GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat
    The storage format of the predictions generated BatchPrediction job.
    bigquery GoogleCloudAiplatformV1beta1BigQueryDestination
    BigQuery location for BatchExplain output.
    gcs GoogleCloudAiplatformV1beta1GcsDestination
    Cloud Storage location for BatchExplain output.
    predictionFormat GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat
    The storage format of the predictions generated BatchPrediction job.
    bigquery GoogleCloudAiplatformV1beta1BigQueryDestination
    BigQuery location for BatchExplain output.
    gcs GoogleCloudAiplatformV1beta1GcsDestination
    Cloud Storage location for BatchExplain output.
    predictionFormat GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat
    The storage format of the predictions generated BatchPrediction job.
    bigquery GoogleCloudAiplatformV1beta1BigQueryDestination
    BigQuery location for BatchExplain output.
    gcs GoogleCloudAiplatformV1beta1GcsDestination
    Cloud Storage location for BatchExplain output.
    prediction_format GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat
    The storage format of the predictions generated BatchPrediction job.
    bigquery Property Map
    BigQuery location for BatchExplain output.
    gcs Property Map
    Cloud Storage location for BatchExplain output.
    predictionFormat "PREDICTION_FORMAT_UNSPECIFIED" | "JSONL" | "BIGQUERY"
    The storage format of the predictions generated BatchPrediction job.

    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormat, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormatArgs

    PredictionFormatUnspecified
    PREDICTION_FORMAT_UNSPECIFIEDShould not be set.
    Jsonl
    JSONLPredictions are in JSONL files.
    Bigquery
    BIGQUERYPredictions are in BigQuery.
    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormatPredictionFormatUnspecified
    PREDICTION_FORMAT_UNSPECIFIEDShould not be set.
    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormatJsonl
    JSONLPredictions are in JSONL files.
    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselinePredictionFormatBigquery
    BIGQUERYPredictions are in BigQuery.
    PredictionFormatUnspecified
    PREDICTION_FORMAT_UNSPECIFIEDShould not be set.
    Jsonl
    JSONLPredictions are in JSONL files.
    Bigquery
    BIGQUERYPredictions are in BigQuery.
    PredictionFormatUnspecified
    PREDICTION_FORMAT_UNSPECIFIEDShould not be set.
    Jsonl
    JSONLPredictions are in JSONL files.
    Bigquery
    BIGQUERYPredictions are in BigQuery.
    PREDICTION_FORMAT_UNSPECIFIED
    PREDICTION_FORMAT_UNSPECIFIEDShould not be set.
    JSONL
    JSONLPredictions are in JSONL files.
    BIGQUERY
    BIGQUERYPredictions are in BigQuery.
    "PREDICTION_FORMAT_UNSPECIFIED"
    PREDICTION_FORMAT_UNSPECIFIEDShould not be set.
    "JSONL"
    JSONLPredictions are in JSONL files.
    "BIGQUERY"
    BIGQUERYPredictions are in BigQuery.

    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponseArgs

    Bigquery Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
    BigQuery location for BatchExplain output.
    Gcs Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationResponse
    Cloud Storage location for BatchExplain output.
    PredictionFormat string
    The storage format of the predictions generated BatchPrediction job.
    Bigquery GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
    BigQuery location for BatchExplain output.
    Gcs GoogleCloudAiplatformV1beta1GcsDestinationResponse
    Cloud Storage location for BatchExplain output.
    PredictionFormat string
    The storage format of the predictions generated BatchPrediction job.
    bigquery GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
    BigQuery location for BatchExplain output.
    gcs GoogleCloudAiplatformV1beta1GcsDestinationResponse
    Cloud Storage location for BatchExplain output.
    predictionFormat String
    The storage format of the predictions generated BatchPrediction job.
    bigquery GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
    BigQuery location for BatchExplain output.
    gcs GoogleCloudAiplatformV1beta1GcsDestinationResponse
    Cloud Storage location for BatchExplain output.
    predictionFormat string
    The storage format of the predictions generated BatchPrediction job.
    bigquery GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
    BigQuery location for BatchExplain output.
    gcs GoogleCloudAiplatformV1beta1GcsDestinationResponse
    Cloud Storage location for BatchExplain output.
    prediction_format str
    The storage format of the predictions generated BatchPrediction job.
    bigquery Property Map
    BigQuery location for BatchExplain output.
    gcs Property Map
    Cloud Storage location for BatchExplain output.
    predictionFormat String
    The storage format of the predictions generated BatchPrediction job.

    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponseArgs

    EnableFeatureAttributes bool
    If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
    ExplanationBaseline Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
    Predictions generated by the BatchPredictionJob using baseline dataset.
    EnableFeatureAttributes bool
    If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
    ExplanationBaseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
    Predictions generated by the BatchPredictionJob using baseline dataset.
    enableFeatureAttributes Boolean
    If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
    explanationBaseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
    Predictions generated by the BatchPredictionJob using baseline dataset.
    enableFeatureAttributes boolean
    If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
    explanationBaseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
    Predictions generated by the BatchPredictionJob using baseline dataset.
    enable_feature_attributes bool
    If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
    explanation_baseline GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigExplanationBaselineResponse
    Predictions generated by the BatchPredictionJob using baseline dataset.
    enableFeatureAttributes Boolean
    If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.
    explanationBaseline Property Map
    Predictions generated by the BatchPredictionJob using baseline dataset.

    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfig, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigArgs

    AttributionScoreDriftThresholds Dictionary<string, string>
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
    DefaultDriftThreshold Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfig
    Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    DriftThresholds Dictionary<string, string>
    Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
    AttributionScoreDriftThresholds map[string]string
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
    DefaultDriftThreshold GoogleCloudAiplatformV1beta1ThresholdConfig
    Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    DriftThresholds map[string]string
    Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
    attributionScoreDriftThresholds Map<String,String>
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
    defaultDriftThreshold GoogleCloudAiplatformV1beta1ThresholdConfig
    Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    driftThresholds Map<String,String>
    Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
    attributionScoreDriftThresholds {[key: string]: string}
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
    defaultDriftThreshold GoogleCloudAiplatformV1beta1ThresholdConfig
    Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    driftThresholds {[key: string]: string}
    Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
    attribution_score_drift_thresholds Mapping[str, str]
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
    default_drift_threshold GoogleCloudAiplatformV1beta1ThresholdConfig
    Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    drift_thresholds Mapping[str, str]
    Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
    attributionScoreDriftThresholds Map<String>
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
    defaultDriftThreshold Property Map
    Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    driftThresholds Map<String>
    Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.

    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponseArgs

    AttributionScoreDriftThresholds Dictionary<string, string>
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
    DefaultDriftThreshold Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfigResponse
    Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    DriftThresholds Dictionary<string, string>
    Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
    AttributionScoreDriftThresholds map[string]string
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
    DefaultDriftThreshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse
    Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    DriftThresholds map[string]string
    Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
    attributionScoreDriftThresholds Map<String,String>
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
    defaultDriftThreshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse
    Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    driftThresholds Map<String,String>
    Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
    attributionScoreDriftThresholds {[key: string]: string}
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
    defaultDriftThreshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse
    Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    driftThresholds {[key: string]: string}
    Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
    attribution_score_drift_thresholds Mapping[str, str]
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
    default_drift_threshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse
    Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    drift_thresholds Mapping[str, str]
    Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.
    attributionScoreDriftThresholds Map<String>
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.
    defaultDriftThreshold Property Map
    Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    driftThresholds Map<String>
    Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.

    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponse, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigResponseArgs

    ExplanationConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigExplanationConfigResponse
    The config for integrating with Vertex Explainable AI.
    PredictionDriftDetectionConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigPredictionDriftDetectionConfigResponse
    The config for drift of prediction data.
    TrainingDataset Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse
    Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
    TrainingPredictionSkewDetectionConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse
    The config for skew between training data and prediction data.
    explanationConfig Property Map
    The config for integrating with Vertex Explainable AI.
    predictionDriftDetectionConfig Property Map
    The config for drift of prediction data.
    trainingDataset Property Map
    Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.
    trainingPredictionSkewDetectionConfig Property Map
    The config for skew between training data and prediction data.

    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDataset, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetArgs

    BigquerySource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQuerySource
    The BigQuery table of the unmanaged Dataset used to train this Model.
    DataFormat string
    Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
    Dataset string
    The resource name of the Dataset used to train this Model.
    GcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSource
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    LoggingSamplingStrategy Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategy
    Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
    TargetField string
    The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
    BigquerySource GoogleCloudAiplatformV1beta1BigQuerySource
    The BigQuery table of the unmanaged Dataset used to train this Model.
    DataFormat string
    Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
    Dataset string
    The resource name of the Dataset used to train this Model.
    GcsSource GoogleCloudAiplatformV1beta1GcsSource
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    LoggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategy
    Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
    TargetField string
    The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
    bigquerySource GoogleCloudAiplatformV1beta1BigQuerySource
    The BigQuery table of the unmanaged Dataset used to train this Model.
    dataFormat String
    Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
    dataset String
    The resource name of the Dataset used to train this Model.
    gcsSource GoogleCloudAiplatformV1beta1GcsSource
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    loggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategy
    Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
    targetField String
    The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
    bigquerySource GoogleCloudAiplatformV1beta1BigQuerySource
    The BigQuery table of the unmanaged Dataset used to train this Model.
    dataFormat string
    Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
    dataset string
    The resource name of the Dataset used to train this Model.
    gcsSource GoogleCloudAiplatformV1beta1GcsSource
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    loggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategy
    Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
    targetField string
    The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
    bigquery_source GoogleCloudAiplatformV1beta1BigQuerySource
    The BigQuery table of the unmanaged Dataset used to train this Model.
    data_format str
    Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
    dataset str
    The resource name of the Dataset used to train this Model.
    gcs_source GoogleCloudAiplatformV1beta1GcsSource
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    logging_sampling_strategy GoogleCloudAiplatformV1beta1SamplingStrategy
    Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
    target_field str
    The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
    bigquerySource Property Map
    The BigQuery table of the unmanaged Dataset used to train this Model.
    dataFormat String
    Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
    dataset String
    The resource name of the Dataset used to train this Model.
    gcsSource Property Map
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    loggingSamplingStrategy Property Map
    Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
    targetField String
    The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.

    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponse, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingDatasetResponseArgs

    BigquerySource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQuerySourceResponse
    The BigQuery table of the unmanaged Dataset used to train this Model.
    DataFormat string
    Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
    Dataset string
    The resource name of the Dataset used to train this Model.
    GcsSource Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    LoggingSamplingStrategy Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SamplingStrategyResponse
    Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
    TargetField string
    The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
    BigquerySource GoogleCloudAiplatformV1beta1BigQuerySourceResponse
    The BigQuery table of the unmanaged Dataset used to train this Model.
    DataFormat string
    Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
    Dataset string
    The resource name of the Dataset used to train this Model.
    GcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    LoggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategyResponse
    Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
    TargetField string
    The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
    bigquerySource GoogleCloudAiplatformV1beta1BigQuerySourceResponse
    The BigQuery table of the unmanaged Dataset used to train this Model.
    dataFormat String
    Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
    dataset String
    The resource name of the Dataset used to train this Model.
    gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    loggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategyResponse
    Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
    targetField String
    The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
    bigquerySource GoogleCloudAiplatformV1beta1BigQuerySourceResponse
    The BigQuery table of the unmanaged Dataset used to train this Model.
    dataFormat string
    Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
    dataset string
    The resource name of the Dataset used to train this Model.
    gcsSource GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    loggingSamplingStrategy GoogleCloudAiplatformV1beta1SamplingStrategyResponse
    Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
    targetField string
    The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
    bigquery_source GoogleCloudAiplatformV1beta1BigQuerySourceResponse
    The BigQuery table of the unmanaged Dataset used to train this Model.
    data_format str
    Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
    dataset str
    The resource name of the Dataset used to train this Model.
    gcs_source GoogleCloudAiplatformV1beta1GcsSourceResponse
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    logging_sampling_strategy GoogleCloudAiplatformV1beta1SamplingStrategyResponse
    Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
    target_field str
    The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.
    bigquerySource Property Map
    The BigQuery table of the unmanaged Dataset used to train this Model.
    dataFormat String
    Data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are: "tf-record" The source file is a TFRecord file. "csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.
    dataset String
    The resource name of the Dataset used to train this Model.
    gcsSource Property Map
    The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.
    loggingSamplingStrategy Property Map
    Strategy to sample data from Training Dataset. If not set, we process the whole dataset.
    targetField String
    The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.

    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfig, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigArgs

    AttributionScoreSkewThresholds Dictionary<string, string>
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
    DefaultSkewThreshold Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfig
    Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    SkewThresholds Dictionary<string, string>
    Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
    AttributionScoreSkewThresholds map[string]string
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
    DefaultSkewThreshold GoogleCloudAiplatformV1beta1ThresholdConfig
    Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    SkewThresholds map[string]string
    Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
    attributionScoreSkewThresholds Map<String,String>
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
    defaultSkewThreshold GoogleCloudAiplatformV1beta1ThresholdConfig
    Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    skewThresholds Map<String,String>
    Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
    attributionScoreSkewThresholds {[key: string]: string}
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
    defaultSkewThreshold GoogleCloudAiplatformV1beta1ThresholdConfig
    Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    skewThresholds {[key: string]: string}
    Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
    attribution_score_skew_thresholds Mapping[str, str]
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
    default_skew_threshold GoogleCloudAiplatformV1beta1ThresholdConfig
    Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    skew_thresholds Mapping[str, str]
    Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
    attributionScoreSkewThresholds Map<String>
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
    defaultSkewThreshold Property Map
    Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    skewThresholds Map<String>
    Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

    GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponse, GoogleCloudAiplatformV1beta1ModelMonitoringObjectiveConfigTrainingPredictionSkewDetectionConfigResponseArgs

    AttributionScoreSkewThresholds Dictionary<string, string>
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
    DefaultSkewThreshold Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ThresholdConfigResponse
    Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    SkewThresholds Dictionary<string, string>
    Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
    AttributionScoreSkewThresholds map[string]string
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
    DefaultSkewThreshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse
    Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    SkewThresholds map[string]string
    Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
    attributionScoreSkewThresholds Map<String,String>
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
    defaultSkewThreshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse
    Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    skewThresholds Map<String,String>
    Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
    attributionScoreSkewThresholds {[key: string]: string}
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
    defaultSkewThreshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse
    Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    skewThresholds {[key: string]: string}
    Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
    attribution_score_skew_thresholds Mapping[str, str]
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
    default_skew_threshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse
    Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    skew_thresholds Mapping[str, str]
    Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.
    attributionScoreSkewThresholds Map<String>
    Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.
    defaultSkewThreshold Property Map
    Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.
    skewThresholds Map<String>
    Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

    GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomalies, GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesArgs

    AnomalyCount int
    Number of anomalies within all stats.
    DeployedModelId string
    Deployed Model ID.
    FeatureStats List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomalies>
    A list of historical Stats and Anomalies generated for all Features.
    Objective Pulumi.GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjective
    Model Monitoring Objective those stats and anomalies belonging to.
    AnomalyCount int
    Number of anomalies within all stats.
    DeployedModelId string
    Deployed Model ID.
    FeatureStats []GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomalies
    A list of historical Stats and Anomalies generated for all Features.
    Objective GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjective
    Model Monitoring Objective those stats and anomalies belonging to.
    anomalyCount Integer
    Number of anomalies within all stats.
    deployedModelId String
    Deployed Model ID.
    featureStats List<GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomalies>
    A list of historical Stats and Anomalies generated for all Features.
    objective GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjective
    Model Monitoring Objective those stats and anomalies belonging to.
    anomalyCount number
    Number of anomalies within all stats.
    deployedModelId string
    Deployed Model ID.
    featureStats GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomalies[]
    A list of historical Stats and Anomalies generated for all Features.
    objective GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjective
    Model Monitoring Objective those stats and anomalies belonging to.
    anomaly_count int
    Number of anomalies within all stats.
    deployed_model_id str
    Deployed Model ID.
    feature_stats Sequence[GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomalies]
    A list of historical Stats and Anomalies generated for all Features.
    objective GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjective
    Model Monitoring Objective those stats and anomalies belonging to.
    anomalyCount Number
    Number of anomalies within all stats.
    deployedModelId String
    Deployed Model ID.
    featureStats List<Property Map>
    A list of historical Stats and Anomalies generated for all Features.
    objective "MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED" | "RAW_FEATURE_SKEW" | "RAW_FEATURE_DRIFT" | "FEATURE_ATTRIBUTION_SKEW" | "FEATURE_ATTRIBUTION_DRIFT"
    Model Monitoring Objective those stats and anomalies belonging to.

    GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomalies, GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesArgs

    FeatureDisplayName string
    Display Name of the Feature.
    PredictionStats []GoogleCloudAiplatformV1beta1FeatureStatsAnomaly
    A list of historical stats generated by different time window's Prediction Dataset.
    Threshold GoogleCloudAiplatformV1beta1ThresholdConfig
    Threshold for anomaly detection.
    TrainingStats GoogleCloudAiplatformV1beta1FeatureStatsAnomaly
    Stats calculated for the Training Dataset.
    featureDisplayName String
    Display Name of the Feature.
    predictionStats List<GoogleCloudAiplatformV1beta1FeatureStatsAnomaly>
    A list of historical stats generated by different time window's Prediction Dataset.
    threshold GoogleCloudAiplatformV1beta1ThresholdConfig
    Threshold for anomaly detection.
    trainingStats GoogleCloudAiplatformV1beta1FeatureStatsAnomaly
    Stats calculated for the Training Dataset.
    featureDisplayName string
    Display Name of the Feature.
    predictionStats GoogleCloudAiplatformV1beta1FeatureStatsAnomaly[]
    A list of historical stats generated by different time window's Prediction Dataset.
    threshold GoogleCloudAiplatformV1beta1ThresholdConfig
    Threshold for anomaly detection.
    trainingStats GoogleCloudAiplatformV1beta1FeatureStatsAnomaly
    Stats calculated for the Training Dataset.
    feature_display_name str
    Display Name of the Feature.
    prediction_stats Sequence[GoogleCloudAiplatformV1beta1FeatureStatsAnomaly]
    A list of historical stats generated by different time window's Prediction Dataset.
    threshold GoogleCloudAiplatformV1beta1ThresholdConfig
    Threshold for anomaly detection.
    training_stats GoogleCloudAiplatformV1beta1FeatureStatsAnomaly
    Stats calculated for the Training Dataset.
    featureDisplayName String
    Display Name of the Feature.
    predictionStats List<Property Map>
    A list of historical stats generated by different time window's Prediction Dataset.
    threshold Property Map
    Threshold for anomaly detection.
    trainingStats Property Map
    Stats calculated for the Training Dataset.

    GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesResponse, GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesResponseArgs

    FeatureDisplayName string
    Display Name of the Feature.
    PredictionStats []GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
    A list of historical stats generated by different time window's Prediction Dataset.
    Threshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse
    Threshold for anomaly detection.
    TrainingStats GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
    Stats calculated for the Training Dataset.
    featureDisplayName String
    Display Name of the Feature.
    predictionStats List<GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse>
    A list of historical stats generated by different time window's Prediction Dataset.
    threshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse
    Threshold for anomaly detection.
    trainingStats GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
    Stats calculated for the Training Dataset.
    featureDisplayName string
    Display Name of the Feature.
    predictionStats GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse[]
    A list of historical stats generated by different time window's Prediction Dataset.
    threshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse
    Threshold for anomaly detection.
    trainingStats GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
    Stats calculated for the Training Dataset.
    feature_display_name str
    Display Name of the Feature.
    prediction_stats Sequence[GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse]
    A list of historical stats generated by different time window's Prediction Dataset.
    threshold GoogleCloudAiplatformV1beta1ThresholdConfigResponse
    Threshold for anomaly detection.
    training_stats GoogleCloudAiplatformV1beta1FeatureStatsAnomalyResponse
    Stats calculated for the Training Dataset.
    featureDisplayName String
    Display Name of the Feature.
    predictionStats List<Property Map>
    A list of historical stats generated by different time window's Prediction Dataset.
    threshold Property Map
    Threshold for anomaly detection.
    trainingStats Property Map
    Stats calculated for the Training Dataset.

    GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjective, GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjectiveArgs

    ModelDeploymentMonitoringObjectiveTypeUnspecified
    MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIEDDefault value, should not be set.
    RawFeatureSkew
    RAW_FEATURE_SKEWRaw feature values' stats to detect skew between Training-Prediction datasets.
    RawFeatureDrift
    RAW_FEATURE_DRIFTRaw feature values' stats to detect drift between Serving-Prediction datasets.
    FeatureAttributionSkew
    FEATURE_ATTRIBUTION_SKEWFeature attribution scores to detect skew between Training-Prediction datasets.
    FeatureAttributionDrift
    FEATURE_ATTRIBUTION_DRIFTFeature attribution scores to detect skew between Prediction datasets collected within different time windows.
    GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjectiveModelDeploymentMonitoringObjectiveTypeUnspecified
    MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIEDDefault value, should not be set.
    GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjectiveRawFeatureSkew
    RAW_FEATURE_SKEWRaw feature values' stats to detect skew between Training-Prediction datasets.
    GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjectiveRawFeatureDrift
    RAW_FEATURE_DRIFTRaw feature values' stats to detect drift between Serving-Prediction datasets.
    GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjectiveFeatureAttributionSkew
    FEATURE_ATTRIBUTION_SKEWFeature attribution scores to detect skew between Training-Prediction datasets.
    GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesObjectiveFeatureAttributionDrift
    FEATURE_ATTRIBUTION_DRIFTFeature attribution scores to detect skew between Prediction datasets collected within different time windows.
    ModelDeploymentMonitoringObjectiveTypeUnspecified
    MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIEDDefault value, should not be set.
    RawFeatureSkew
    RAW_FEATURE_SKEWRaw feature values' stats to detect skew between Training-Prediction datasets.
    RawFeatureDrift
    RAW_FEATURE_DRIFTRaw feature values' stats to detect drift between Serving-Prediction datasets.
    FeatureAttributionSkew
    FEATURE_ATTRIBUTION_SKEWFeature attribution scores to detect skew between Training-Prediction datasets.
    FeatureAttributionDrift
    FEATURE_ATTRIBUTION_DRIFTFeature attribution scores to detect skew between Prediction datasets collected within different time windows.
    ModelDeploymentMonitoringObjectiveTypeUnspecified
    MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIEDDefault value, should not be set.
    RawFeatureSkew
    RAW_FEATURE_SKEWRaw feature values' stats to detect skew between Training-Prediction datasets.
    RawFeatureDrift
    RAW_FEATURE_DRIFTRaw feature values' stats to detect drift between Serving-Prediction datasets.
    FeatureAttributionSkew
    FEATURE_ATTRIBUTION_SKEWFeature attribution scores to detect skew between Training-Prediction datasets.
    FeatureAttributionDrift
    FEATURE_ATTRIBUTION_DRIFTFeature attribution scores to detect skew between Prediction datasets collected within different time windows.
    MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED
    MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIEDDefault value, should not be set.
    RAW_FEATURE_SKEW
    RAW_FEATURE_SKEWRaw feature values' stats to detect skew between Training-Prediction datasets.
    RAW_FEATURE_DRIFT
    RAW_FEATURE_DRIFTRaw feature values' stats to detect drift between Serving-Prediction datasets.
    FEATURE_ATTRIBUTION_SKEW
    FEATURE_ATTRIBUTION_SKEWFeature attribution scores to detect skew between Training-Prediction datasets.
    FEATURE_ATTRIBUTION_DRIFT
    FEATURE_ATTRIBUTION_DRIFTFeature attribution scores to detect skew between Prediction datasets collected within different time windows.
    "MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIED"
    MODEL_DEPLOYMENT_MONITORING_OBJECTIVE_TYPE_UNSPECIFIEDDefault value, should not be set.
    "RAW_FEATURE_SKEW"
    RAW_FEATURE_SKEWRaw feature values' stats to detect skew between Training-Prediction datasets.
    "RAW_FEATURE_DRIFT"
    RAW_FEATURE_DRIFTRaw feature values' stats to detect drift between Serving-Prediction datasets.
    "FEATURE_ATTRIBUTION_SKEW"
    FEATURE_ATTRIBUTION_SKEWFeature attribution scores to detect skew between Training-Prediction datasets.
    "FEATURE_ATTRIBUTION_DRIFT"
    FEATURE_ATTRIBUTION_DRIFTFeature attribution scores to detect skew between Prediction datasets collected within different time windows.

    GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesResponse, GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesResponseArgs

    AnomalyCount int
    Number of anomalies within all stats.
    DeployedModelId string
    Deployed Model ID.
    FeatureStats List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesResponse>
    A list of historical Stats and Anomalies generated for all Features.
    Objective string
    Model Monitoring Objective those stats and anomalies belonging to.
    AnomalyCount int
    Number of anomalies within all stats.
    DeployedModelId string
    Deployed Model ID.
    FeatureStats []GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesResponse
    A list of historical Stats and Anomalies generated for all Features.
    Objective string
    Model Monitoring Objective those stats and anomalies belonging to.
    anomalyCount Integer
    Number of anomalies within all stats.
    deployedModelId String
    Deployed Model ID.
    featureStats List<GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesResponse>
    A list of historical Stats and Anomalies generated for all Features.
    objective String
    Model Monitoring Objective those stats and anomalies belonging to.
    anomalyCount number
    Number of anomalies within all stats.
    deployedModelId string
    Deployed Model ID.
    featureStats GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesResponse[]
    A list of historical Stats and Anomalies generated for all Features.
    objective string
    Model Monitoring Objective those stats and anomalies belonging to.
    anomaly_count int
    Number of anomalies within all stats.
    deployed_model_id str
    Deployed Model ID.
    feature_stats Sequence[GoogleCloudAiplatformV1beta1ModelMonitoringStatsAnomaliesFeatureHistoricStatsAnomaliesResponse]
    A list of historical Stats and Anomalies generated for all Features.
    objective str
    Model Monitoring Objective those stats and anomalies belonging to.
    anomalyCount Number
    Number of anomalies within all stats.
    deployedModelId String
    Deployed Model ID.
    featureStats List<Property Map>
    A list of historical Stats and Anomalies generated for all Features.
    objective String
    Model Monitoring Objective those stats and anomalies belonging to.

    GoogleCloudAiplatformV1beta1Port, GoogleCloudAiplatformV1beta1PortArgs

    ContainerPort int
    The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
    ContainerPort int
    The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
    containerPort Integer
    The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
    containerPort number
    The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
    container_port int
    The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
    containerPort Number
    The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.

    GoogleCloudAiplatformV1beta1PortResponse, GoogleCloudAiplatformV1beta1PortResponseArgs

    ContainerPort int
    The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
    ContainerPort int
    The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
    containerPort Integer
    The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
    containerPort number
    The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
    container_port int
    The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
    containerPort Number
    The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.

    GoogleCloudAiplatformV1beta1PredictSchemata, GoogleCloudAiplatformV1beta1PredictSchemataArgs

    InstanceSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    ParametersSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    PredictionSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    InstanceSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    ParametersSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    PredictionSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    instanceSchemaUri String
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    parametersSchemaUri String
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    predictionSchemaUri String
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    instanceSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    parametersSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    predictionSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    instance_schema_uri str
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    parameters_schema_uri str
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    prediction_schema_uri str
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    instanceSchemaUri String
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    parametersSchemaUri String
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    predictionSchemaUri String
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

    GoogleCloudAiplatformV1beta1PredictSchemataResponse, GoogleCloudAiplatformV1beta1PredictSchemataResponseArgs

    InstanceSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    ParametersSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    PredictionSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    InstanceSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    ParametersSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    PredictionSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    instanceSchemaUri String
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    parametersSchemaUri String
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    predictionSchemaUri String
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    instanceSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    parametersSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    predictionSchemaUri string
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    instance_schema_uri str
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    parameters_schema_uri str
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    prediction_schema_uri str
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    instanceSchemaUri String
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    parametersSchemaUri String
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no parameters are supported, then it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
    predictionSchemaUri String
    Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

    GoogleCloudAiplatformV1beta1Presets, GoogleCloudAiplatformV1beta1PresetsArgs

    Modality Pulumi.GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1PresetsModality
    The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
    Query Pulumi.GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1PresetsQuery
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
    Modality GoogleCloudAiplatformV1beta1PresetsModality
    The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
    Query GoogleCloudAiplatformV1beta1PresetsQuery
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
    modality GoogleCloudAiplatformV1beta1PresetsModality
    The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
    query GoogleCloudAiplatformV1beta1PresetsQuery
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
    modality GoogleCloudAiplatformV1beta1PresetsModality
    The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
    query GoogleCloudAiplatformV1beta1PresetsQuery
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
    modality GoogleCloudAiplatformV1beta1PresetsModality
    The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
    query GoogleCloudAiplatformV1beta1PresetsQuery
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
    modality "MODALITY_UNSPECIFIED" | "IMAGE" | "TEXT" | "TABULAR"
    The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
    query "PRECISE" | "FAST"
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.

    GoogleCloudAiplatformV1beta1PresetsModality, GoogleCloudAiplatformV1beta1PresetsModalityArgs

    ModalityUnspecified
    MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
    Image
    IMAGEIMAGE modality
    Text
    TEXTTEXT modality
    Tabular
    TABULARTABULAR modality
    GoogleCloudAiplatformV1beta1PresetsModalityModalityUnspecified
    MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
    GoogleCloudAiplatformV1beta1PresetsModalityImage
    IMAGEIMAGE modality
    GoogleCloudAiplatformV1beta1PresetsModalityText
    TEXTTEXT modality
    GoogleCloudAiplatformV1beta1PresetsModalityTabular
    TABULARTABULAR modality
    ModalityUnspecified
    MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
    Image
    IMAGEIMAGE modality
    Text
    TEXTTEXT modality
    Tabular
    TABULARTABULAR modality
    ModalityUnspecified
    MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
    Image
    IMAGEIMAGE modality
    Text
    TEXTTEXT modality
    Tabular
    TABULARTABULAR modality
    MODALITY_UNSPECIFIED
    MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
    IMAGE
    IMAGEIMAGE modality
    TEXT
    TEXTTEXT modality
    TABULAR
    TABULARTABULAR modality
    "MODALITY_UNSPECIFIED"
    MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
    "IMAGE"
    IMAGEIMAGE modality
    "TEXT"
    TEXTTEXT modality
    "TABULAR"
    TABULARTABULAR modality

    GoogleCloudAiplatformV1beta1PresetsQuery, GoogleCloudAiplatformV1beta1PresetsQueryArgs

    Precise
    PRECISEMore precise neighbors as a trade-off against slower response.
    Fast
    FASTFaster response as a trade-off against less precise neighbors.
    GoogleCloudAiplatformV1beta1PresetsQueryPrecise
    PRECISEMore precise neighbors as a trade-off against slower response.
    GoogleCloudAiplatformV1beta1PresetsQueryFast
    FASTFaster response as a trade-off against less precise neighbors.
    Precise
    PRECISEMore precise neighbors as a trade-off against slower response.
    Fast
    FASTFaster response as a trade-off against less precise neighbors.
    Precise
    PRECISEMore precise neighbors as a trade-off against slower response.
    Fast
    FASTFaster response as a trade-off against less precise neighbors.
    PRECISE
    PRECISEMore precise neighbors as a trade-off against slower response.
    FAST
    FASTFaster response as a trade-off against less precise neighbors.
    "PRECISE"
    PRECISEMore precise neighbors as a trade-off against slower response.
    "FAST"
    FASTFaster response as a trade-off against less precise neighbors.

    GoogleCloudAiplatformV1beta1PresetsResponse, GoogleCloudAiplatformV1beta1PresetsResponseArgs

    Modality string
    The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
    Query string
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
    Modality string
    The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
    Query string
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
    modality String
    The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
    query String
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
    modality string
    The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
    query string
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
    modality str
    The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
    query str
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
    modality String
    The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
    query String
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.

    GoogleCloudAiplatformV1beta1Probe, GoogleCloudAiplatformV1beta1ProbeArgs

    Exec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeExecAction
    Exec specifies the action to take.
    PeriodSeconds int
    How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
    TimeoutSeconds int
    Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
    Exec GoogleCloudAiplatformV1beta1ProbeExecAction
    Exec specifies the action to take.
    PeriodSeconds int
    How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
    TimeoutSeconds int
    Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
    exec GoogleCloudAiplatformV1beta1ProbeExecAction
    Exec specifies the action to take.
    periodSeconds Integer
    How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
    timeoutSeconds Integer
    Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
    exec GoogleCloudAiplatformV1beta1ProbeExecAction
    Exec specifies the action to take.
    periodSeconds number
    How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
    timeoutSeconds number
    Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
    exec_ GoogleCloudAiplatformV1beta1ProbeExecAction
    Exec specifies the action to take.
    period_seconds int
    How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
    timeout_seconds int
    Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
    exec Property Map
    Exec specifies the action to take.
    periodSeconds Number
    How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
    timeoutSeconds Number
    Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.

    GoogleCloudAiplatformV1beta1ProbeExecAction, GoogleCloudAiplatformV1beta1ProbeExecActionArgs

    Command List<string>
    Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
    Command []string
    Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
    command List<String>
    Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
    command string[]
    Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
    command Sequence[str]
    Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
    command List<String>
    Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.

    GoogleCloudAiplatformV1beta1ProbeExecActionResponse, GoogleCloudAiplatformV1beta1ProbeExecActionResponseArgs

    Command List<string>
    Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
    Command []string
    Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
    command List<String>
    Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
    command string[]
    Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
    command Sequence[str]
    Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
    command List<String>
    Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.

    GoogleCloudAiplatformV1beta1ProbeResponse, GoogleCloudAiplatformV1beta1ProbeResponseArgs

    Exec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeExecActionResponse
    Exec specifies the action to take.
    PeriodSeconds int
    How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
    TimeoutSeconds int
    Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
    Exec GoogleCloudAiplatformV1beta1ProbeExecActionResponse
    Exec specifies the action to take.
    PeriodSeconds int
    How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
    TimeoutSeconds int
    Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
    exec GoogleCloudAiplatformV1beta1ProbeExecActionResponse
    Exec specifies the action to take.
    periodSeconds Integer
    How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
    timeoutSeconds Integer
    Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
    exec GoogleCloudAiplatformV1beta1ProbeExecActionResponse
    Exec specifies the action to take.
    periodSeconds number
    How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
    timeoutSeconds number
    Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
    exec_ GoogleCloudAiplatformV1beta1ProbeExecActionResponse
    Exec specifies the action to take.
    period_seconds int
    How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
    timeout_seconds int
    Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
    exec Property Map
    Exec specifies the action to take.
    periodSeconds Number
    How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. Must be less than timeout_seconds. Maps to Kubernetes probe argument 'periodSeconds'.
    timeoutSeconds Number
    Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.

    GoogleCloudAiplatformV1beta1ResourcesConsumedResponse, GoogleCloudAiplatformV1beta1ResourcesConsumedResponseArgs

    ReplicaHours double
    The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.
    ReplicaHours float64
    The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.
    replicaHours Double
    The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.
    replicaHours number
    The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.
    replica_hours float
    The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.
    replicaHours Number
    The number of replica hours used. Note that many replicas may run in parallel, and additionally any given work may be queued for some time. Therefore this value is not strictly related to wall time.

    GoogleCloudAiplatformV1beta1SampledShapleyAttribution, GoogleCloudAiplatformV1beta1SampledShapleyAttributionArgs

    PathCount int
    The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
    PathCount int
    The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
    pathCount Integer
    The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
    pathCount number
    The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
    path_count int
    The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
    pathCount Number
    The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.

    GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse, GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponseArgs

    PathCount int
    The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
    PathCount int
    The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
    pathCount Integer
    The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
    pathCount number
    The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
    path_count int
    The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
    pathCount Number
    The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.

    GoogleCloudAiplatformV1beta1SamplingStrategy, GoogleCloudAiplatformV1beta1SamplingStrategyArgs

    RandomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfig
    Random sample config. Will support more sampling strategies later.
    randomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfig
    Random sample config. Will support more sampling strategies later.
    randomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfig
    Random sample config. Will support more sampling strategies later.
    random_sample_config GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfig
    Random sample config. Will support more sampling strategies later.
    randomSampleConfig Property Map
    Random sample config. Will support more sampling strategies later.

    GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfig, GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigArgs

    SampleRate double
    Sample rate (0, 1]
    SampleRate float64
    Sample rate (0, 1]
    sampleRate Double
    Sample rate (0, 1]
    sampleRate number
    Sample rate (0, 1]
    sample_rate float
    Sample rate (0, 1]
    sampleRate Number
    Sample rate (0, 1]

    GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse, GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponseArgs

    SampleRate double
    Sample rate (0, 1]
    SampleRate float64
    Sample rate (0, 1]
    sampleRate Double
    Sample rate (0, 1]
    sampleRate number
    Sample rate (0, 1]
    sample_rate float
    Sample rate (0, 1]
    sampleRate Number
    Sample rate (0, 1]

    GoogleCloudAiplatformV1beta1SamplingStrategyResponse, GoogleCloudAiplatformV1beta1SamplingStrategyResponseArgs

    RandomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse
    Random sample config. Will support more sampling strategies later.
    randomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse
    Random sample config. Will support more sampling strategies later.
    randomSampleConfig GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse
    Random sample config. Will support more sampling strategies later.
    random_sample_config GoogleCloudAiplatformV1beta1SamplingStrategyRandomSampleConfigResponse
    Random sample config. Will support more sampling strategies later.
    randomSampleConfig Property Map
    Random sample config. Will support more sampling strategies later.

    GoogleCloudAiplatformV1beta1SmoothGradConfig, GoogleCloudAiplatformV1beta1SmoothGradConfigArgs

    FeatureNoiseSigma Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureNoiseSigma
    This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
    NoiseSigma double
    This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
    NoisySampleCount int
    The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
    FeatureNoiseSigma GoogleCloudAiplatformV1beta1FeatureNoiseSigma
    This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
    NoiseSigma float64
    This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
    NoisySampleCount int
    The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
    featureNoiseSigma GoogleCloudAiplatformV1beta1FeatureNoiseSigma
    This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
    noiseSigma Double
    This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
    noisySampleCount Integer
    The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
    featureNoiseSigma GoogleCloudAiplatformV1beta1FeatureNoiseSigma
    This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
    noiseSigma number
    This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
    noisySampleCount number
    The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
    feature_noise_sigma GoogleCloudAiplatformV1beta1FeatureNoiseSigma
    This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
    noise_sigma float
    This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
    noisy_sample_count int
    The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
    featureNoiseSigma Property Map
    This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
    noiseSigma Number
    This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
    noisySampleCount Number
    The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.

    GoogleCloudAiplatformV1beta1SmoothGradConfigResponse, GoogleCloudAiplatformV1beta1SmoothGradConfigResponseArgs

    FeatureNoiseSigma Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse
    This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
    NoiseSigma double
    This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
    NoisySampleCount int
    The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
    FeatureNoiseSigma GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse
    This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
    NoiseSigma float64
    This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
    NoisySampleCount int
    The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
    featureNoiseSigma GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse
    This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
    noiseSigma Double
    This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
    noisySampleCount Integer
    The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
    featureNoiseSigma GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse
    This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
    noiseSigma number
    This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
    noisySampleCount number
    The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
    feature_noise_sigma GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse
    This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
    noise_sigma float
    This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
    noisy_sample_count int
    The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
    featureNoiseSigma Property Map
    This is similar to noise_sigma, but provides additional flexibility. A separate noise sigma can be provided for each feature, which is useful if their distributions are different. No noise is added to features that are not set. If this field is unset, noise_sigma will be used for all features.
    noiseSigma Number
    This is a single float value and will be used to add noise to all the features. Use this field when all features are normalized to have the same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where features are normalized to have 0-mean and 1-variance. Learn more about normalization. For best results the recommended value is about 10% - 20% of the standard deviation of the input feature. Refer to section 3.2 of the SmoothGrad paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1. If the distribution is different per feature, set feature_noise_sigma instead for each feature.
    noisySampleCount Number
    The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.

    GoogleCloudAiplatformV1beta1ThresholdConfig, GoogleCloudAiplatformV1beta1ThresholdConfigArgs

    Value double
    Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    Value float64
    Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value Double
    Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value number
    Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value float
    Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value Number
    Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.

    GoogleCloudAiplatformV1beta1ThresholdConfigResponse, GoogleCloudAiplatformV1beta1ThresholdConfigResponseArgs

    Value double
    Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    Value float64
    Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value Double
    Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value number
    Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value float
    Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.
    value Number
    Specify a threshold value that can trigger the alert. If this threshold config is for feature distribution distance: 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.

    GoogleCloudAiplatformV1beta1UnmanagedContainerModel, GoogleCloudAiplatformV1beta1UnmanagedContainerModelArgs

    ArtifactUri string
    The path to the directory containing the Model artifact and any of its supporting files.
    ContainerSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelContainerSpec
    Input only. The specification of the container that is to be used when deploying this Model.
    PredictSchemata Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PredictSchemata
    Contains the schemata used in Model's predictions and explanations
    ArtifactUri string
    The path to the directory containing the Model artifact and any of its supporting files.
    ContainerSpec GoogleCloudAiplatformV1beta1ModelContainerSpec
    Input only. The specification of the container that is to be used when deploying this Model.
    PredictSchemata GoogleCloudAiplatformV1beta1PredictSchemata
    Contains the schemata used in Model's predictions and explanations
    artifactUri String
    The path to the directory containing the Model artifact and any of its supporting files.
    containerSpec GoogleCloudAiplatformV1beta1ModelContainerSpec
    Input only. The specification of the container that is to be used when deploying this Model.
    predictSchemata GoogleCloudAiplatformV1beta1PredictSchemata
    Contains the schemata used in Model's predictions and explanations
    artifactUri string
    The path to the directory containing the Model artifact and any of its supporting files.
    containerSpec GoogleCloudAiplatformV1beta1ModelContainerSpec
    Input only. The specification of the container that is to be used when deploying this Model.
    predictSchemata GoogleCloudAiplatformV1beta1PredictSchemata
    Contains the schemata used in Model's predictions and explanations
    artifact_uri str
    The path to the directory containing the Model artifact and any of its supporting files.
    container_spec GoogleCloudAiplatformV1beta1ModelContainerSpec
    Input only. The specification of the container that is to be used when deploying this Model.
    predict_schemata GoogleCloudAiplatformV1beta1PredictSchemata
    Contains the schemata used in Model's predictions and explanations
    artifactUri String
    The path to the directory containing the Model artifact and any of its supporting files.
    containerSpec Property Map
    Input only. The specification of the container that is to be used when deploying this Model.
    predictSchemata Property Map
    Contains the schemata used in Model's predictions and explanations

    GoogleCloudAiplatformV1beta1UnmanagedContainerModelResponse, GoogleCloudAiplatformV1beta1UnmanagedContainerModelResponseArgs

    ArtifactUri string
    The path to the directory containing the Model artifact and any of its supporting files.
    ContainerSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelContainerSpecResponse
    Input only. The specification of the container that is to be used when deploying this Model.
    PredictSchemata Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PredictSchemataResponse
    Contains the schemata used in Model's predictions and explanations
    ArtifactUri string
    The path to the directory containing the Model artifact and any of its supporting files.
    ContainerSpec GoogleCloudAiplatformV1beta1ModelContainerSpecResponse
    Input only. The specification of the container that is to be used when deploying this Model.
    PredictSchemata GoogleCloudAiplatformV1beta1PredictSchemataResponse
    Contains the schemata used in Model's predictions and explanations
    artifactUri String
    The path to the directory containing the Model artifact and any of its supporting files.
    containerSpec GoogleCloudAiplatformV1beta1ModelContainerSpecResponse
    Input only. The specification of the container that is to be used when deploying this Model.
    predictSchemata GoogleCloudAiplatformV1beta1PredictSchemataResponse
    Contains the schemata used in Model's predictions and explanations
    artifactUri string
    The path to the directory containing the Model artifact and any of its supporting files.
    containerSpec GoogleCloudAiplatformV1beta1ModelContainerSpecResponse
    Input only. The specification of the container that is to be used when deploying this Model.
    predictSchemata GoogleCloudAiplatformV1beta1PredictSchemataResponse
    Contains the schemata used in Model's predictions and explanations
    artifact_uri str
    The path to the directory containing the Model artifact and any of its supporting files.
    container_spec GoogleCloudAiplatformV1beta1ModelContainerSpecResponse
    Input only. The specification of the container that is to be used when deploying this Model.
    predict_schemata GoogleCloudAiplatformV1beta1PredictSchemataResponse
    Contains the schemata used in Model's predictions and explanations
    artifactUri String
    The path to the directory containing the Model artifact and any of its supporting files.
    containerSpec Property Map
    Input only. The specification of the container that is to be used when deploying this Model.
    predictSchemata Property Map
    Contains the schemata used in Model's predictions and explanations

    GoogleCloudAiplatformV1beta1XraiAttribution, GoogleCloudAiplatformV1beta1XraiAttributionArgs

    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    BlurBaselineConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BlurBaselineConfig
    Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    SmoothGradConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    BlurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfig
    Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    SmoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Integer
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfig
    Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfig
    Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    step_count int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blur_baseline_config GoogleCloudAiplatformV1beta1BlurBaselineConfig
    Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smooth_grad_config GoogleCloudAiplatformV1beta1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig Property Map
    Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig Property Map
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

    GoogleCloudAiplatformV1beta1XraiAttributionResponse, GoogleCloudAiplatformV1beta1XraiAttributionResponseArgs

    BlurBaselineConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse
    Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    SmoothGradConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    BlurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse
    Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    SmoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse
    Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Integer
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse
    Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig GoogleCloudAiplatformV1beta1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blur_baseline_config GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse
    Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smooth_grad_config GoogleCloudAiplatformV1beta1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    step_count int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig Property Map
    Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig Property Map
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.

    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.

    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