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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

google-native.aiplatform/v1beta1.TrainingPipeline

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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 TrainingPipeline. A created TrainingPipeline right away will be attempted to be run. Auto-naming is currently not supported for this resource.

Create TrainingPipeline Resource

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

Constructor syntax

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

@overload
def TrainingPipeline(resource_name: str,
                     opts: Optional[ResourceOptions] = None,
                     display_name: Optional[str] = None,
                     training_task_definition: Optional[str] = None,
                     training_task_inputs: Optional[Any] = None,
                     encryption_spec: Optional[GoogleCloudAiplatformV1beta1EncryptionSpecArgs] = None,
                     input_data_config: Optional[GoogleCloudAiplatformV1beta1InputDataConfigArgs] = None,
                     labels: Optional[Mapping[str, str]] = None,
                     location: Optional[str] = None,
                     model_id: Optional[str] = None,
                     model_to_upload: Optional[GoogleCloudAiplatformV1beta1ModelArgs] = None,
                     parent_model: Optional[str] = None,
                     project: Optional[str] = None)
func NewTrainingPipeline(ctx *Context, name string, args TrainingPipelineArgs, opts ...ResourceOption) (*TrainingPipeline, error)
public TrainingPipeline(string name, TrainingPipelineArgs args, CustomResourceOptions? opts = null)
public TrainingPipeline(String name, TrainingPipelineArgs args)
public TrainingPipeline(String name, TrainingPipelineArgs args, CustomResourceOptions options)

type: google-native:aiplatform/v1beta1:TrainingPipeline
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

Parameters

name This property is required. string
The unique name of the resource.
args This property is required. TrainingPipelineArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
resource_name This property is required. str
The unique name of the resource.
args This property is required. TrainingPipelineArgs
The arguments to resource properties.
opts ResourceOptions
Bag of options to control resource's behavior.
ctx Context
Context object for the current deployment.
name This property is required. string
The unique name of the resource.
args This property is required. TrainingPipelineArgs
The arguments to resource properties.
opts ResourceOption
Bag of options to control resource's behavior.
name This property is required. string
The unique name of the resource.
args This property is required. TrainingPipelineArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
name This property is required. String
The unique name of the resource.
args This property is required. TrainingPipelineArgs
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_nativeTrainingPipelineResource = new GoogleNative.Aiplatform.V1Beta1.TrainingPipeline("google-nativeTrainingPipelineResource", new()
{
    DisplayName = "string",
    TrainingTaskDefinition = "string",
    TrainingTaskInputs = "any",
    EncryptionSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EncryptionSpecArgs
    {
        KmsKeyName = "string",
    },
    InputDataConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1InputDataConfigArgs
    {
        DatasetId = "string",
        AnnotationSchemaUri = "string",
        AnnotationsFilter = "string",
        BigqueryDestination = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestinationArgs
        {
            OutputUri = "string",
        },
        FilterSplit = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FilterSplitArgs
        {
            TestFilter = "string",
            TrainingFilter = "string",
            ValidationFilter = "string",
        },
        FractionSplit = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FractionSplitArgs
        {
            TestFraction = 0,
            TrainingFraction = 0,
            ValidationFraction = 0,
        },
        GcsDestination = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationArgs
        {
            OutputUriPrefix = "string",
        },
        PersistMlUseAssignment = false,
        PredefinedSplit = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PredefinedSplitArgs
        {
            Key = "string",
        },
        SavedQueryId = "string",
        StratifiedSplit = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StratifiedSplitArgs
        {
            Key = "string",
            TestFraction = 0,
            TrainingFraction = 0,
            ValidationFraction = 0,
        },
        TimestampSplit = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1TimestampSplitArgs
        {
            Key = "string",
            TestFraction = 0,
            TrainingFraction = 0,
            ValidationFraction = 0,
        },
    },
    Labels = 
    {
        { "string", "string" },
    },
    Location = "string",
    ModelId = "string",
    ModelToUpload = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelArgs
    {
        DisplayName = "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",
            },
        },
        Description = "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,
            },
        },
        EncryptionSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EncryptionSpecArgs
        {
            KmsKeyName = "string",
        },
        Etag = "string",
        ArtifactUri = "string",
        Labels = 
        {
            { "string", "string" },
        },
        Metadata = "any",
        MetadataSchemaUri = "string",
        Name = "string",
        PredictSchemata = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PredictSchemataArgs
        {
            InstanceSchemaUri = "string",
            ParametersSchemaUri = "string",
            PredictionSchemaUri = "string",
        },
        VersionAliases = new[]
        {
            "string",
        },
        VersionDescription = "string",
    },
    ParentModel = "string",
    Project = "string",
});
Copy
example, err := aiplatformv1beta1.NewTrainingPipeline(ctx, "google-nativeTrainingPipelineResource", &aiplatformv1beta1.TrainingPipelineArgs{
	DisplayName:            pulumi.String("string"),
	TrainingTaskDefinition: pulumi.String("string"),
	TrainingTaskInputs:     pulumi.Any("any"),
	EncryptionSpec: &aiplatform.GoogleCloudAiplatformV1beta1EncryptionSpecArgs{
		KmsKeyName: pulumi.String("string"),
	},
	InputDataConfig: &aiplatform.GoogleCloudAiplatformV1beta1InputDataConfigArgs{
		DatasetId:           pulumi.String("string"),
		AnnotationSchemaUri: pulumi.String("string"),
		AnnotationsFilter:   pulumi.String("string"),
		BigqueryDestination: &aiplatform.GoogleCloudAiplatformV1beta1BigQueryDestinationArgs{
			OutputUri: pulumi.String("string"),
		},
		FilterSplit: &aiplatform.GoogleCloudAiplatformV1beta1FilterSplitArgs{
			TestFilter:       pulumi.String("string"),
			TrainingFilter:   pulumi.String("string"),
			ValidationFilter: pulumi.String("string"),
		},
		FractionSplit: &aiplatform.GoogleCloudAiplatformV1beta1FractionSplitArgs{
			TestFraction:       pulumi.Float64(0),
			TrainingFraction:   pulumi.Float64(0),
			ValidationFraction: pulumi.Float64(0),
		},
		GcsDestination: &aiplatform.GoogleCloudAiplatformV1beta1GcsDestinationArgs{
			OutputUriPrefix: pulumi.String("string"),
		},
		PersistMlUseAssignment: pulumi.Bool(false),
		PredefinedSplit: &aiplatform.GoogleCloudAiplatformV1beta1PredefinedSplitArgs{
			Key: pulumi.String("string"),
		},
		SavedQueryId: pulumi.String("string"),
		StratifiedSplit: &aiplatform.GoogleCloudAiplatformV1beta1StratifiedSplitArgs{
			Key:                pulumi.String("string"),
			TestFraction:       pulumi.Float64(0),
			TrainingFraction:   pulumi.Float64(0),
			ValidationFraction: pulumi.Float64(0),
		},
		TimestampSplit: &aiplatform.GoogleCloudAiplatformV1beta1TimestampSplitArgs{
			Key:                pulumi.String("string"),
			TestFraction:       pulumi.Float64(0),
			TrainingFraction:   pulumi.Float64(0),
			ValidationFraction: pulumi.Float64(0),
		},
	},
	Labels: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	Location: pulumi.String("string"),
	ModelId:  pulumi.String("string"),
	ModelToUpload: &aiplatform.GoogleCloudAiplatformV1beta1ModelArgs{
		DisplayName: 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"),
			},
		},
		Description: 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),
			},
		},
		EncryptionSpec: &aiplatform.GoogleCloudAiplatformV1beta1EncryptionSpecArgs{
			KmsKeyName: pulumi.String("string"),
		},
		Etag:        pulumi.String("string"),
		ArtifactUri: pulumi.String("string"),
		Labels: pulumi.StringMap{
			"string": pulumi.String("string"),
		},
		Metadata:          pulumi.Any("any"),
		MetadataSchemaUri: pulumi.String("string"),
		Name:              pulumi.String("string"),
		PredictSchemata: &aiplatform.GoogleCloudAiplatformV1beta1PredictSchemataArgs{
			InstanceSchemaUri:   pulumi.String("string"),
			ParametersSchemaUri: pulumi.String("string"),
			PredictionSchemaUri: pulumi.String("string"),
		},
		VersionAliases: pulumi.StringArray{
			pulumi.String("string"),
		},
		VersionDescription: pulumi.String("string"),
	},
	ParentModel: pulumi.String("string"),
	Project:     pulumi.String("string"),
})
Copy
var google_nativeTrainingPipelineResource = new com.pulumi.googlenative.aiplatform_v1beta1.TrainingPipeline("google-nativeTrainingPipelineResource", com.pulumi.googlenative.aiplatform_v1beta1.TrainingPipelineArgs.builder()
    .displayName("string")
    .trainingTaskDefinition("string")
    .trainingTaskInputs("any")
    .encryptionSpec(GoogleCloudAiplatformV1beta1EncryptionSpecArgs.builder()
        .kmsKeyName("string")
        .build())
    .inputDataConfig(GoogleCloudAiplatformV1beta1InputDataConfigArgs.builder()
        .datasetId("string")
        .annotationSchemaUri("string")
        .annotationsFilter("string")
        .bigqueryDestination(GoogleCloudAiplatformV1beta1BigQueryDestinationArgs.builder()
            .outputUri("string")
            .build())
        .filterSplit(GoogleCloudAiplatformV1beta1FilterSplitArgs.builder()
            .testFilter("string")
            .trainingFilter("string")
            .validationFilter("string")
            .build())
        .fractionSplit(GoogleCloudAiplatformV1beta1FractionSplitArgs.builder()
            .testFraction(0)
            .trainingFraction(0)
            .validationFraction(0)
            .build())
        .gcsDestination(GoogleCloudAiplatformV1beta1GcsDestinationArgs.builder()
            .outputUriPrefix("string")
            .build())
        .persistMlUseAssignment(false)
        .predefinedSplit(GoogleCloudAiplatformV1beta1PredefinedSplitArgs.builder()
            .key("string")
            .build())
        .savedQueryId("string")
        .stratifiedSplit(GoogleCloudAiplatformV1beta1StratifiedSplitArgs.builder()
            .key("string")
            .testFraction(0)
            .trainingFraction(0)
            .validationFraction(0)
            .build())
        .timestampSplit(GoogleCloudAiplatformV1beta1TimestampSplitArgs.builder()
            .key("string")
            .testFraction(0)
            .trainingFraction(0)
            .validationFraction(0)
            .build())
        .build())
    .labels(Map.of("string", "string"))
    .location("string")
    .modelId("string")
    .modelToUpload(GoogleCloudAiplatformV1beta1ModelArgs.builder()
        .displayName("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())
        .description("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())
        .encryptionSpec(GoogleCloudAiplatformV1beta1EncryptionSpecArgs.builder()
            .kmsKeyName("string")
            .build())
        .etag("string")
        .artifactUri("string")
        .labels(Map.of("string", "string"))
        .metadata("any")
        .metadataSchemaUri("string")
        .name("string")
        .predictSchemata(GoogleCloudAiplatformV1beta1PredictSchemataArgs.builder()
            .instanceSchemaUri("string")
            .parametersSchemaUri("string")
            .predictionSchemaUri("string")
            .build())
        .versionAliases("string")
        .versionDescription("string")
        .build())
    .parentModel("string")
    .project("string")
    .build());
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google_native_training_pipeline_resource = google_native.aiplatform.v1beta1.TrainingPipeline("google-nativeTrainingPipelineResource",
    display_name="string",
    training_task_definition="string",
    training_task_inputs="any",
    encryption_spec={
        "kms_key_name": "string",
    },
    input_data_config={
        "dataset_id": "string",
        "annotation_schema_uri": "string",
        "annotations_filter": "string",
        "bigquery_destination": {
            "output_uri": "string",
        },
        "filter_split": {
            "test_filter": "string",
            "training_filter": "string",
            "validation_filter": "string",
        },
        "fraction_split": {
            "test_fraction": 0,
            "training_fraction": 0,
            "validation_fraction": 0,
        },
        "gcs_destination": {
            "output_uri_prefix": "string",
        },
        "persist_ml_use_assignment": False,
        "predefined_split": {
            "key": "string",
        },
        "saved_query_id": "string",
        "stratified_split": {
            "key": "string",
            "test_fraction": 0,
            "training_fraction": 0,
            "validation_fraction": 0,
        },
        "timestamp_split": {
            "key": "string",
            "test_fraction": 0,
            "training_fraction": 0,
            "validation_fraction": 0,
        },
    },
    labels={
        "string": "string",
    },
    location="string",
    model_id="string",
    model_to_upload={
        "display_name": "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",
            },
        },
        "description": "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,
            },
        },
        "encryption_spec": {
            "kms_key_name": "string",
        },
        "etag": "string",
        "artifact_uri": "string",
        "labels": {
            "string": "string",
        },
        "metadata": "any",
        "metadata_schema_uri": "string",
        "name": "string",
        "predict_schemata": {
            "instance_schema_uri": "string",
            "parameters_schema_uri": "string",
            "prediction_schema_uri": "string",
        },
        "version_aliases": ["string"],
        "version_description": "string",
    },
    parent_model="string",
    project="string")
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const google_nativeTrainingPipelineResource = new google_native.aiplatform.v1beta1.TrainingPipeline("google-nativeTrainingPipelineResource", {
    displayName: "string",
    trainingTaskDefinition: "string",
    trainingTaskInputs: "any",
    encryptionSpec: {
        kmsKeyName: "string",
    },
    inputDataConfig: {
        datasetId: "string",
        annotationSchemaUri: "string",
        annotationsFilter: "string",
        bigqueryDestination: {
            outputUri: "string",
        },
        filterSplit: {
            testFilter: "string",
            trainingFilter: "string",
            validationFilter: "string",
        },
        fractionSplit: {
            testFraction: 0,
            trainingFraction: 0,
            validationFraction: 0,
        },
        gcsDestination: {
            outputUriPrefix: "string",
        },
        persistMlUseAssignment: false,
        predefinedSplit: {
            key: "string",
        },
        savedQueryId: "string",
        stratifiedSplit: {
            key: "string",
            testFraction: 0,
            trainingFraction: 0,
            validationFraction: 0,
        },
        timestampSplit: {
            key: "string",
            testFraction: 0,
            trainingFraction: 0,
            validationFraction: 0,
        },
    },
    labels: {
        string: "string",
    },
    location: "string",
    modelId: "string",
    modelToUpload: {
        displayName: "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",
            },
        },
        description: "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,
            },
        },
        encryptionSpec: {
            kmsKeyName: "string",
        },
        etag: "string",
        artifactUri: "string",
        labels: {
            string: "string",
        },
        metadata: "any",
        metadataSchemaUri: "string",
        name: "string",
        predictSchemata: {
            instanceSchemaUri: "string",
            parametersSchemaUri: "string",
            predictionSchemaUri: "string",
        },
        versionAliases: ["string"],
        versionDescription: "string",
    },
    parentModel: "string",
    project: "string",
});
Copy
type: google-native:aiplatform/v1beta1:TrainingPipeline
properties:
    displayName: string
    encryptionSpec:
        kmsKeyName: string
    inputDataConfig:
        annotationSchemaUri: string
        annotationsFilter: string
        bigqueryDestination:
            outputUri: string
        datasetId: string
        filterSplit:
            testFilter: string
            trainingFilter: string
            validationFilter: string
        fractionSplit:
            testFraction: 0
            trainingFraction: 0
            validationFraction: 0
        gcsDestination:
            outputUriPrefix: string
        persistMlUseAssignment: false
        predefinedSplit:
            key: string
        savedQueryId: string
        stratifiedSplit:
            key: string
            testFraction: 0
            trainingFraction: 0
            validationFraction: 0
        timestampSplit:
            key: string
            testFraction: 0
            trainingFraction: 0
            validationFraction: 0
    labels:
        string: string
    location: string
    modelId: string
    modelToUpload:
        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
        description: string
        displayName: string
        encryptionSpec:
            kmsKeyName: string
        etag: 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
        labels:
            string: string
        metadata: any
        metadataSchemaUri: string
        name: string
        predictSchemata:
            instanceSchemaUri: string
            parametersSchemaUri: string
            predictionSchemaUri: string
        versionAliases:
            - string
        versionDescription: string
    parentModel: string
    project: string
    trainingTaskDefinition: string
    trainingTaskInputs: any
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TrainingPipeline 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 TrainingPipeline resource accepts the following input properties:

DisplayName This property is required. string
The user-defined name of this TrainingPipeline.
TrainingTaskDefinition This property is required. string
A Google Cloud Storage path to the YAML file that defines the training task which is responsible for producing the model artifact, and may also include additional auxiliary work. The definition files that can be used here are found in gs://google-cloud-aiplatform/schema/trainingjob/definition/. 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.
TrainingTaskInputs This property is required. object
The training task's parameter(s), as specified in the training_task_definition's inputs.
EncryptionSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EncryptionSpec
Customer-managed encryption key spec for a TrainingPipeline. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.
InputDataConfig Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1InputDataConfig
Specifies Vertex AI owned input data that may be used for training the Model. The TrainingPipeline's training_task_definition should make clear whether this config is used and if there are any special requirements on how it should be filled. If nothing about this config is mentioned in the training_task_definition, then it should be assumed that the TrainingPipeline does not depend on this configuration.
Labels Dictionary<string, string>
The labels with user-defined metadata to organize TrainingPipelines. 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 Changes to this property will trigger replacement. string
ModelId string
Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.
ModelToUpload Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1Model
Describes the Model that may be uploaded (via ModelService.UploadModel) by this TrainingPipeline. The TrainingPipeline's training_task_definition should make clear whether this Model description should be populated, and if there are any special requirements regarding how it should be filled. If nothing is mentioned in the training_task_definition, then it should be assumed that this field should not be filled and the training task either uploads the Model without a need of this information, or that training task does not support uploading a Model as part of the pipeline. When the Pipeline's state becomes PIPELINE_STATE_SUCCEEDED and the trained Model had been uploaded into Vertex AI, then the model_to_upload's resource name is populated. The Model is always uploaded into the Project and Location in which this pipeline is.
ParentModel string
Optional. When specify this field, the model_to_upload will not be uploaded as a new model, instead, it will become a new version of this parent_model.
Project Changes to this property will trigger replacement. string
DisplayName This property is required. string
The user-defined name of this TrainingPipeline.
TrainingTaskDefinition This property is required. string
A Google Cloud Storage path to the YAML file that defines the training task which is responsible for producing the model artifact, and may also include additional auxiliary work. The definition files that can be used here are found in gs://google-cloud-aiplatform/schema/trainingjob/definition/. 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.
TrainingTaskInputs This property is required. interface{}
The training task's parameter(s), as specified in the training_task_definition's inputs.
EncryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecArgs
Customer-managed encryption key spec for a TrainingPipeline. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.
InputDataConfig GoogleCloudAiplatformV1beta1InputDataConfigArgs
Specifies Vertex AI owned input data that may be used for training the Model. The TrainingPipeline's training_task_definition should make clear whether this config is used and if there are any special requirements on how it should be filled. If nothing about this config is mentioned in the training_task_definition, then it should be assumed that the TrainingPipeline does not depend on this configuration.
Labels map[string]string
The labels with user-defined metadata to organize TrainingPipelines. 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 Changes to this property will trigger replacement. string
ModelId string
Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.
ModelToUpload GoogleCloudAiplatformV1beta1ModelArgs
Describes the Model that may be uploaded (via ModelService.UploadModel) by this TrainingPipeline. The TrainingPipeline's training_task_definition should make clear whether this Model description should be populated, and if there are any special requirements regarding how it should be filled. If nothing is mentioned in the training_task_definition, then it should be assumed that this field should not be filled and the training task either uploads the Model without a need of this information, or that training task does not support uploading a Model as part of the pipeline. When the Pipeline's state becomes PIPELINE_STATE_SUCCEEDED and the trained Model had been uploaded into Vertex AI, then the model_to_upload's resource name is populated. The Model is always uploaded into the Project and Location in which this pipeline is.
ParentModel string
Optional. When specify this field, the model_to_upload will not be uploaded as a new model, instead, it will become a new version of this parent_model.
Project Changes to this property will trigger replacement. string
displayName This property is required. String
The user-defined name of this TrainingPipeline.
trainingTaskDefinition This property is required. String
A Google Cloud Storage path to the YAML file that defines the training task which is responsible for producing the model artifact, and may also include additional auxiliary work. The definition files that can be used here are found in gs://google-cloud-aiplatform/schema/trainingjob/definition/. 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.
trainingTaskInputs This property is required. Object
The training task's parameter(s), as specified in the training_task_definition's inputs.
encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpec
Customer-managed encryption key spec for a TrainingPipeline. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.
inputDataConfig GoogleCloudAiplatformV1beta1InputDataConfig
Specifies Vertex AI owned input data that may be used for training the Model. The TrainingPipeline's training_task_definition should make clear whether this config is used and if there are any special requirements on how it should be filled. If nothing about this config is mentioned in the training_task_definition, then it should be assumed that the TrainingPipeline does not depend on this configuration.
labels Map<String,String>
The labels with user-defined metadata to organize TrainingPipelines. 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 Changes to this property will trigger replacement. String
modelId String
Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.
modelToUpload GoogleCloudAiplatformV1beta1Model
Describes the Model that may be uploaded (via ModelService.UploadModel) by this TrainingPipeline. The TrainingPipeline's training_task_definition should make clear whether this Model description should be populated, and if there are any special requirements regarding how it should be filled. If nothing is mentioned in the training_task_definition, then it should be assumed that this field should not be filled and the training task either uploads the Model without a need of this information, or that training task does not support uploading a Model as part of the pipeline. When the Pipeline's state becomes PIPELINE_STATE_SUCCEEDED and the trained Model had been uploaded into Vertex AI, then the model_to_upload's resource name is populated. The Model is always uploaded into the Project and Location in which this pipeline is.
parentModel String
Optional. When specify this field, the model_to_upload will not be uploaded as a new model, instead, it will become a new version of this parent_model.
project Changes to this property will trigger replacement. String
displayName This property is required. string
The user-defined name of this TrainingPipeline.
trainingTaskDefinition This property is required. string
A Google Cloud Storage path to the YAML file that defines the training task which is responsible for producing the model artifact, and may also include additional auxiliary work. The definition files that can be used here are found in gs://google-cloud-aiplatform/schema/trainingjob/definition/. 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.
trainingTaskInputs This property is required. any
The training task's parameter(s), as specified in the training_task_definition's inputs.
encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpec
Customer-managed encryption key spec for a TrainingPipeline. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.
inputDataConfig GoogleCloudAiplatformV1beta1InputDataConfig
Specifies Vertex AI owned input data that may be used for training the Model. The TrainingPipeline's training_task_definition should make clear whether this config is used and if there are any special requirements on how it should be filled. If nothing about this config is mentioned in the training_task_definition, then it should be assumed that the TrainingPipeline does not depend on this configuration.
labels {[key: string]: string}
The labels with user-defined metadata to organize TrainingPipelines. 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 Changes to this property will trigger replacement. string
modelId string
Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.
modelToUpload GoogleCloudAiplatformV1beta1Model
Describes the Model that may be uploaded (via ModelService.UploadModel) by this TrainingPipeline. The TrainingPipeline's training_task_definition should make clear whether this Model description should be populated, and if there are any special requirements regarding how it should be filled. If nothing is mentioned in the training_task_definition, then it should be assumed that this field should not be filled and the training task either uploads the Model without a need of this information, or that training task does not support uploading a Model as part of the pipeline. When the Pipeline's state becomes PIPELINE_STATE_SUCCEEDED and the trained Model had been uploaded into Vertex AI, then the model_to_upload's resource name is populated. The Model is always uploaded into the Project and Location in which this pipeline is.
parentModel string
Optional. When specify this field, the model_to_upload will not be uploaded as a new model, instead, it will become a new version of this parent_model.
project Changes to this property will trigger replacement. string
display_name This property is required. str
The user-defined name of this TrainingPipeline.
training_task_definition This property is required. str
A Google Cloud Storage path to the YAML file that defines the training task which is responsible for producing the model artifact, and may also include additional auxiliary work. The definition files that can be used here are found in gs://google-cloud-aiplatform/schema/trainingjob/definition/. 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.
training_task_inputs This property is required. Any
The training task's parameter(s), as specified in the training_task_definition's inputs.
encryption_spec GoogleCloudAiplatformV1beta1EncryptionSpecArgs
Customer-managed encryption key spec for a TrainingPipeline. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.
input_data_config GoogleCloudAiplatformV1beta1InputDataConfigArgs
Specifies Vertex AI owned input data that may be used for training the Model. The TrainingPipeline's training_task_definition should make clear whether this config is used and if there are any special requirements on how it should be filled. If nothing about this config is mentioned in the training_task_definition, then it should be assumed that the TrainingPipeline does not depend on this configuration.
labels Mapping[str, str]
The labels with user-defined metadata to organize TrainingPipelines. 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 Changes to this property will trigger replacement. str
model_id str
Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.
model_to_upload GoogleCloudAiplatformV1beta1ModelArgs
Describes the Model that may be uploaded (via ModelService.UploadModel) by this TrainingPipeline. The TrainingPipeline's training_task_definition should make clear whether this Model description should be populated, and if there are any special requirements regarding how it should be filled. If nothing is mentioned in the training_task_definition, then it should be assumed that this field should not be filled and the training task either uploads the Model without a need of this information, or that training task does not support uploading a Model as part of the pipeline. When the Pipeline's state becomes PIPELINE_STATE_SUCCEEDED and the trained Model had been uploaded into Vertex AI, then the model_to_upload's resource name is populated. The Model is always uploaded into the Project and Location in which this pipeline is.
parent_model str
Optional. When specify this field, the model_to_upload will not be uploaded as a new model, instead, it will become a new version of this parent_model.
project Changes to this property will trigger replacement. str
displayName This property is required. String
The user-defined name of this TrainingPipeline.
trainingTaskDefinition This property is required. String
A Google Cloud Storage path to the YAML file that defines the training task which is responsible for producing the model artifact, and may also include additional auxiliary work. The definition files that can be used here are found in gs://google-cloud-aiplatform/schema/trainingjob/definition/. 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.
trainingTaskInputs This property is required. Any
The training task's parameter(s), as specified in the training_task_definition's inputs.
encryptionSpec Property Map
Customer-managed encryption key spec for a TrainingPipeline. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately.
inputDataConfig Property Map
Specifies Vertex AI owned input data that may be used for training the Model. The TrainingPipeline's training_task_definition should make clear whether this config is used and if there are any special requirements on how it should be filled. If nothing about this config is mentioned in the training_task_definition, then it should be assumed that the TrainingPipeline does not depend on this configuration.
labels Map<String>
The labels with user-defined metadata to organize TrainingPipelines. 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 Changes to this property will trigger replacement. String
modelId String
Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.
modelToUpload Property Map
Describes the Model that may be uploaded (via ModelService.UploadModel) by this TrainingPipeline. The TrainingPipeline's training_task_definition should make clear whether this Model description should be populated, and if there are any special requirements regarding how it should be filled. If nothing is mentioned in the training_task_definition, then it should be assumed that this field should not be filled and the training task either uploads the Model without a need of this information, or that training task does not support uploading a Model as part of the pipeline. When the Pipeline's state becomes PIPELINE_STATE_SUCCEEDED and the trained Model had been uploaded into Vertex AI, then the model_to_upload's resource name is populated. The Model is always uploaded into the Project and Location in which this pipeline is.
parentModel String
Optional. When specify this field, the model_to_upload will not be uploaded as a new model, instead, it will become a new version of this parent_model.
project Changes to this property will trigger replacement. String

Outputs

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

CreateTime string
Time when the TrainingPipeline was created.
EndTime string
Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED.
Error Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleRpcStatusResponse
Only populated when the pipeline's state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.
Id string
The provider-assigned unique ID for this managed resource.
Name string
Resource name of the TrainingPipeline.
StartTime string
Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state.
State string
The detailed state of the pipeline.
TrainingTaskMetadata object
The metadata information as specified in the training_task_definition's metadata. This metadata is an auxiliary runtime and final information about the training task. While the pipeline is running this information is populated only at a best effort basis. Only present if the pipeline's training_task_definition contains metadata object.
UpdateTime string
Time when the TrainingPipeline was most recently updated.
CreateTime string
Time when the TrainingPipeline was created.
EndTime string
Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED.
Error GoogleRpcStatusResponse
Only populated when the pipeline's state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.
Id string
The provider-assigned unique ID for this managed resource.
Name string
Resource name of the TrainingPipeline.
StartTime string
Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state.
State string
The detailed state of the pipeline.
TrainingTaskMetadata interface{}
The metadata information as specified in the training_task_definition's metadata. This metadata is an auxiliary runtime and final information about the training task. While the pipeline is running this information is populated only at a best effort basis. Only present if the pipeline's training_task_definition contains metadata object.
UpdateTime string
Time when the TrainingPipeline was most recently updated.
createTime String
Time when the TrainingPipeline was created.
endTime String
Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED.
error GoogleRpcStatusResponse
Only populated when the pipeline's state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.
id String
The provider-assigned unique ID for this managed resource.
name String
Resource name of the TrainingPipeline.
startTime String
Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state.
state String
The detailed state of the pipeline.
trainingTaskMetadata Object
The metadata information as specified in the training_task_definition's metadata. This metadata is an auxiliary runtime and final information about the training task. While the pipeline is running this information is populated only at a best effort basis. Only present if the pipeline's training_task_definition contains metadata object.
updateTime String
Time when the TrainingPipeline was most recently updated.
createTime string
Time when the TrainingPipeline was created.
endTime string
Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED.
error GoogleRpcStatusResponse
Only populated when the pipeline's state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.
id string
The provider-assigned unique ID for this managed resource.
name string
Resource name of the TrainingPipeline.
startTime string
Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state.
state string
The detailed state of the pipeline.
trainingTaskMetadata any
The metadata information as specified in the training_task_definition's metadata. This metadata is an auxiliary runtime and final information about the training task. While the pipeline is running this information is populated only at a best effort basis. Only present if the pipeline's training_task_definition contains metadata object.
updateTime string
Time when the TrainingPipeline was most recently updated.
create_time str
Time when the TrainingPipeline was created.
end_time str
Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED.
error GoogleRpcStatusResponse
Only populated when the pipeline's state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.
id str
The provider-assigned unique ID for this managed resource.
name str
Resource name of the TrainingPipeline.
start_time str
Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state.
state str
The detailed state of the pipeline.
training_task_metadata Any
The metadata information as specified in the training_task_definition's metadata. This metadata is an auxiliary runtime and final information about the training task. While the pipeline is running this information is populated only at a best effort basis. Only present if the pipeline's training_task_definition contains metadata object.
update_time str
Time when the TrainingPipeline was most recently updated.
createTime String
Time when the TrainingPipeline was created.
endTime String
Time when the TrainingPipeline entered any of the following states: PIPELINE_STATE_SUCCEEDED, PIPELINE_STATE_FAILED, PIPELINE_STATE_CANCELLED.
error Property Map
Only populated when the pipeline's state is PIPELINE_STATE_FAILED or PIPELINE_STATE_CANCELLED.
id String
The provider-assigned unique ID for this managed resource.
name String
Resource name of the TrainingPipeline.
startTime String
Time when the TrainingPipeline for the first time entered the PIPELINE_STATE_RUNNING state.
state String
The detailed state of the pipeline.
trainingTaskMetadata Any
The metadata information as specified in the training_task_definition's metadata. This metadata is an auxiliary runtime and final information about the training task. While the pipeline is running this information is populated only at a best effort basis. Only present if the pipeline's training_task_definition contains metadata object.
updateTime String
Time when the TrainingPipeline was most recently updated.

Supporting Types

GoogleCloudAiplatformV1beta1BigQueryDestination
, GoogleCloudAiplatformV1beta1BigQueryDestinationArgs

OutputUri This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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.

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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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.

GoogleCloudAiplatformV1beta1DeployedModelRefResponse
, GoogleCloudAiplatformV1beta1DeployedModelRefResponseArgs

DeployedModelId This property is required. string
Immutable. An ID of a DeployedModel in the above Endpoint.
Endpoint This property is required. string
Immutable. A resource name of an Endpoint.
DeployedModelId This property is required. string
Immutable. An ID of a DeployedModel in the above Endpoint.
Endpoint This property is required. string
Immutable. A resource name of an Endpoint.
deployedModelId This property is required. String
Immutable. An ID of a DeployedModel in the above Endpoint.
endpoint This property is required. String
Immutable. A resource name of an Endpoint.
deployedModelId This property is required. string
Immutable. An ID of a DeployedModel in the above Endpoint.
endpoint This property is required. string
Immutable. A resource name of an Endpoint.
deployed_model_id This property is required. str
Immutable. An ID of a DeployedModel in the above Endpoint.
endpoint This property is required. str
Immutable. A resource name of an Endpoint.
deployedModelId This property is required. String
Immutable. An ID of a DeployedModel in the above Endpoint.
endpoint This property is required. String
Immutable. A resource name of an Endpoint.

GoogleCloudAiplatformV1beta1EncryptionSpec
, GoogleCloudAiplatformV1beta1EncryptionSpecArgs

KmsKeyName This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. string
Name of the environment variable. Must be a valid C identifier.
Value This property is required. 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 This property is required. string
Name of the environment variable. Must be a valid C identifier.
Value This property is required. 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 This property is required. String
Name of the environment variable. Must be a valid C identifier.
value This property is required. 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 This property is required. string
Name of the environment variable. Must be a valid C identifier.
value This property is required. 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 This property is required. str
Name of the environment variable. Must be a valid C identifier.
value This property is required. 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 This property is required. String
Name of the environment variable. Must be a valid C identifier.
value This property is required. 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 This property is required. string
Name of the environment variable. Must be a valid C identifier.
Value This property is required. 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 This property is required. string
Name of the environment variable. Must be a valid C identifier.
Value This property is required. 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 This property is required. String
Name of the environment variable. Must be a valid C identifier.
value This property is required. 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 This property is required. string
Name of the environment variable. Must be a valid C identifier.
value This property is required. 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 This property is required. str
Name of the environment variable. Must be a valid C identifier.
value This property is required. 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 This property is required. String
Name of the environment variable. Must be a valid C identifier.
value This property is required. 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 This property is required. string
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
GcsSource This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceResponse
The Cloud Storage location for the input instances.
DataFormat This property is required. string
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
GcsSource This property is required. GoogleCloudAiplatformV1beta1GcsSourceResponse
The Cloud Storage location for the input instances.
dataFormat This property is required. String
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource This property is required. GoogleCloudAiplatformV1beta1GcsSourceResponse
The Cloud Storage location for the input instances.
dataFormat This property is required. string
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource This property is required. GoogleCloudAiplatformV1beta1GcsSourceResponse
The Cloud Storage location for the input instances.
data_format This property is required. str
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcs_source This property is required. GoogleCloudAiplatformV1beta1GcsSourceResponse
The Cloud Storage location for the input instances.
dataFormat This property is required. String
The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcsSource This property is required. Property Map
The Cloud Storage location for the input instances.

GoogleCloudAiplatformV1beta1ExamplesResponse
, GoogleCloudAiplatformV1beta1ExamplesResponseArgs

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

GoogleCloudAiplatformV1beta1ExplanationMetadata
, GoogleCloudAiplatformV1beta1ExplanationMetadataArgs

Inputs This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. {[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 This property is required. {[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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. string
Name of the source to generate embeddings for example based explanations.
Outputs This property is required. 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 This property is required. 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 This property is required. 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 This property is required. string
Name of the source to generate embeddings for example based explanations.
Outputs This property is required. 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 This property is required. 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 This property is required. 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 This property is required. String
Name of the source to generate embeddings for example based explanations.
outputs This property is required. 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 This property is required. 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 This property is required. {[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 This property is required. string
Name of the source to generate embeddings for example based explanations.
outputs This property is required. {[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 This property is required. 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 This property is required. 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 This property is required. str
Name of the source to generate embeddings for example based explanations.
outputs This property is required. 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 This property is required. 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 This property is required. 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 This property is required. String
Name of the source to generate embeddings for example based explanations.
outputs This property is required. 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 This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExamplesResponse
Example-based explanations that returns the nearest neighbors from the provided dataset.
IntegratedGradientsAttribution This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ExamplesResponse
Example-based explanations that returns the nearest neighbors from the provided dataset.
IntegratedGradientsAttribution This property is required. 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 This property is required. []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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ExamplesResponse
Example-based explanations that returns the nearest neighbors from the provided dataset.
integratedGradientsAttribution This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ExamplesResponse
Example-based explanations that returns the nearest neighbors from the provided dataset.
integratedGradientsAttribution This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ExamplesResponse
Example-based explanations that returns the nearest neighbors from the provided dataset.
integrated_gradients_attribution This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. Property Map
Example-based explanations that returns the nearest neighbors from the provided dataset.
integratedGradientsAttribution This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ExplanationParameters
Parameters that configure explaining of the Model's predictions.
Metadata GoogleCloudAiplatformV1beta1ExplanationMetadata
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. GoogleCloudAiplatformV1beta1ExplanationParameters
Parameters that configure explaining of the Model's predictions.
metadata GoogleCloudAiplatformV1beta1ExplanationMetadata
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. GoogleCloudAiplatformV1beta1ExplanationParameters
Parameters that configure explaining of the Model's predictions.
metadata GoogleCloudAiplatformV1beta1ExplanationMetadata
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. GoogleCloudAiplatformV1beta1ExplanationParameters
Parameters that configure explaining of the Model's predictions.
metadata GoogleCloudAiplatformV1beta1ExplanationMetadata
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. 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 This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationMetadataResponse
Optional. Metadata describing the Model's input and output for explanation.
Parameters This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationParametersResponse
Parameters that configure explaining of the Model's predictions.
Metadata This property is required. GoogleCloudAiplatformV1beta1ExplanationMetadataResponse
Optional. Metadata describing the Model's input and output for explanation.
Parameters This property is required. GoogleCloudAiplatformV1beta1ExplanationParametersResponse
Parameters that configure explaining of the Model's predictions.
metadata This property is required. GoogleCloudAiplatformV1beta1ExplanationMetadataResponse
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. GoogleCloudAiplatformV1beta1ExplanationParametersResponse
Parameters that configure explaining of the Model's predictions.
metadata This property is required. GoogleCloudAiplatformV1beta1ExplanationMetadataResponse
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. GoogleCloudAiplatformV1beta1ExplanationParametersResponse
Parameters that configure explaining of the Model's predictions.
metadata This property is required. GoogleCloudAiplatformV1beta1ExplanationMetadataResponse
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. GoogleCloudAiplatformV1beta1ExplanationParametersResponse
Parameters that configure explaining of the Model's predictions.
metadata This property is required. Property Map
Optional. Metadata describing the Model's input and output for explanation.
parameters This property is required. 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 This property is required. string
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
Sigma This property is required. 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 This property is required. string
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
Sigma This property is required. 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 This property is required. String
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma This property is required. 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 This property is required. string
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma This property is required. 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 This property is required. str
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma This property is required. 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 This property is required. String
The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma This property is required. 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 This property is required. List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse>
Noise sigma per feature. No noise is added to features that are not set.
NoiseSigma This property is required. []GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse
Noise sigma per feature. No noise is added to features that are not set.
noiseSigma This property is required. List<GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse>
Noise sigma per feature. No noise is added to features that are not set.
noiseSigma This property is required. GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse[]
Noise sigma per feature. No noise is added to features that are not set.
noise_sigma This property is required. Sequence[GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse]
Noise sigma per feature. No noise is added to features that are not set.
noiseSigma This property is required. List<Property Map>
Noise sigma per feature. No noise is added to features that are not set.

GoogleCloudAiplatformV1beta1FilterSplit
, GoogleCloudAiplatformV1beta1FilterSplitArgs

TestFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
TrainingFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
ValidationFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
TestFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
TrainingFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
ValidationFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
testFilter This property is required. String
A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
trainingFilter This property is required. String
A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
validationFilter This property is required. String
A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
testFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
trainingFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
validationFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
test_filter This property is required. str
A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
training_filter This property is required. str
A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
validation_filter This property is required. str
A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
testFilter This property is required. String
A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
trainingFilter This property is required. String
A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
validationFilter This property is required. String
A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.

GoogleCloudAiplatformV1beta1FilterSplitResponse
, GoogleCloudAiplatformV1beta1FilterSplitResponseArgs

TestFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
TrainingFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
ValidationFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
TestFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
TrainingFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
ValidationFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
testFilter This property is required. String
A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
trainingFilter This property is required. String
A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
validationFilter This property is required. String
A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
testFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
trainingFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
validationFilter This property is required. string
A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
test_filter This property is required. str
A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
training_filter This property is required. str
A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
validation_filter This property is required. str
A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
testFilter This property is required. String
A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
trainingFilter This property is required. String
A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
validationFilter This property is required. String
A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.

GoogleCloudAiplatformV1beta1FractionSplit
, GoogleCloudAiplatformV1beta1FractionSplitArgs

TestFraction double
The fraction of the input data that is to be used to evaluate the Model.
TrainingFraction double
The fraction of the input data that is to be used to train the Model.
ValidationFraction double
The fraction of the input data that is to be used to validate the Model.
TestFraction float64
The fraction of the input data that is to be used to evaluate the Model.
TrainingFraction float64
The fraction of the input data that is to be used to train the Model.
ValidationFraction float64
The fraction of the input data that is to be used to validate the Model.
testFraction Double
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction Double
The fraction of the input data that is to be used to train the Model.
validationFraction Double
The fraction of the input data that is to be used to validate the Model.
testFraction number
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction number
The fraction of the input data that is to be used to train the Model.
validationFraction number
The fraction of the input data that is to be used to validate the Model.
test_fraction float
The fraction of the input data that is to be used to evaluate the Model.
training_fraction float
The fraction of the input data that is to be used to train the Model.
validation_fraction float
The fraction of the input data that is to be used to validate the Model.
testFraction Number
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction Number
The fraction of the input data that is to be used to train the Model.
validationFraction Number
The fraction of the input data that is to be used to validate the Model.

GoogleCloudAiplatformV1beta1FractionSplitResponse
, GoogleCloudAiplatformV1beta1FractionSplitResponseArgs

TestFraction This property is required. double
The fraction of the input data that is to be used to evaluate the Model.
TrainingFraction This property is required. double
The fraction of the input data that is to be used to train the Model.
ValidationFraction This property is required. double
The fraction of the input data that is to be used to validate the Model.
TestFraction This property is required. float64
The fraction of the input data that is to be used to evaluate the Model.
TrainingFraction This property is required. float64
The fraction of the input data that is to be used to train the Model.
ValidationFraction This property is required. float64
The fraction of the input data that is to be used to validate the Model.
testFraction This property is required. Double
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction This property is required. Double
The fraction of the input data that is to be used to train the Model.
validationFraction This property is required. Double
The fraction of the input data that is to be used to validate the Model.
testFraction This property is required. number
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction This property is required. number
The fraction of the input data that is to be used to train the Model.
validationFraction This property is required. number
The fraction of the input data that is to be used to validate the Model.
test_fraction This property is required. float
The fraction of the input data that is to be used to evaluate the Model.
training_fraction This property is required. float
The fraction of the input data that is to be used to train the Model.
validation_fraction This property is required. float
The fraction of the input data that is to be used to validate the Model.
testFraction This property is required. Number
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction This property is required. Number
The fraction of the input data that is to be used to train the Model.
validationFraction This property is required. Number
The fraction of the input data that is to be used to validate the Model.

GoogleCloudAiplatformV1beta1GcsDestination
, GoogleCloudAiplatformV1beta1GcsDestinationArgs

OutputUriPrefix This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. []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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. []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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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.

GoogleCloudAiplatformV1beta1InputDataConfig
, GoogleCloudAiplatformV1beta1InputDataConfigArgs

DatasetId This property is required. string
The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
AnnotationSchemaUri string
Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
AnnotationsFilter string
Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
BigqueryDestination Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestination
Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset___ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. * AIP_DATA_FORMAT = "bigquery". * AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" * AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" * AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"
FilterSplit Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FilterSplit
Split based on the provided filters for each set.
FractionSplit Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FractionSplit
Split based on fractions defining the size of each set.
GcsDestination Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestination
The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset--- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-.jsonl" * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-.${AIP_DATA_FORMAT}" * AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-.${AIP_DATA_FORMAT}" * AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-.${AIP_DATA_FORMAT}"
PersistMlUseAssignment bool
Whether to persist the ML use assignment to data item system labels.
PredefinedSplit Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PredefinedSplit
Supported only for tabular Datasets. Split based on a predefined key.
SavedQueryId string
Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
StratifiedSplit Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StratifiedSplit
Supported only for tabular Datasets. Split based on the distribution of the specified column.
TimestampSplit Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1TimestampSplit
Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
DatasetId This property is required. string
The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
AnnotationSchemaUri string
Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
AnnotationsFilter string
Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
BigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestination
Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset___ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. * AIP_DATA_FORMAT = "bigquery". * AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" * AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" * AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"
FilterSplit GoogleCloudAiplatformV1beta1FilterSplit
Split based on the provided filters for each set.
FractionSplit GoogleCloudAiplatformV1beta1FractionSplit
Split based on fractions defining the size of each set.
GcsDestination GoogleCloudAiplatformV1beta1GcsDestination
The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset--- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-.jsonl" * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-.${AIP_DATA_FORMAT}" * AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-.${AIP_DATA_FORMAT}" * AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-.${AIP_DATA_FORMAT}"
PersistMlUseAssignment bool
Whether to persist the ML use assignment to data item system labels.
PredefinedSplit GoogleCloudAiplatformV1beta1PredefinedSplit
Supported only for tabular Datasets. Split based on a predefined key.
SavedQueryId string
Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
StratifiedSplit GoogleCloudAiplatformV1beta1StratifiedSplit
Supported only for tabular Datasets. Split based on the distribution of the specified column.
TimestampSplit GoogleCloudAiplatformV1beta1TimestampSplit
Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
datasetId This property is required. String
The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
annotationSchemaUri String
Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
annotationsFilter String
Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
bigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestination
Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset___ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. * AIP_DATA_FORMAT = "bigquery". * AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" * AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" * AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"
filterSplit GoogleCloudAiplatformV1beta1FilterSplit
Split based on the provided filters for each set.
fractionSplit GoogleCloudAiplatformV1beta1FractionSplit
Split based on fractions defining the size of each set.
gcsDestination GoogleCloudAiplatformV1beta1GcsDestination
The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset--- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-.jsonl" * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-.${AIP_DATA_FORMAT}" * AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-.${AIP_DATA_FORMAT}" * AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-.${AIP_DATA_FORMAT}"
persistMlUseAssignment Boolean
Whether to persist the ML use assignment to data item system labels.
predefinedSplit GoogleCloudAiplatformV1beta1PredefinedSplit
Supported only for tabular Datasets. Split based on a predefined key.
savedQueryId String
Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
stratifiedSplit GoogleCloudAiplatformV1beta1StratifiedSplit
Supported only for tabular Datasets. Split based on the distribution of the specified column.
timestampSplit GoogleCloudAiplatformV1beta1TimestampSplit
Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
datasetId This property is required. string
The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
annotationSchemaUri string
Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
annotationsFilter string
Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
bigqueryDestination GoogleCloudAiplatformV1beta1BigQueryDestination
Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset___ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. * AIP_DATA_FORMAT = "bigquery". * AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" * AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" * AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"
filterSplit GoogleCloudAiplatformV1beta1FilterSplit
Split based on the provided filters for each set.
fractionSplit GoogleCloudAiplatformV1beta1FractionSplit
Split based on fractions defining the size of each set.
gcsDestination GoogleCloudAiplatformV1beta1GcsDestination
The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset--- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-.jsonl" * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-.${AIP_DATA_FORMAT}" * AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-.${AIP_DATA_FORMAT}" * AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-.${AIP_DATA_FORMAT}"
persistMlUseAssignment boolean
Whether to persist the ML use assignment to data item system labels.
predefinedSplit GoogleCloudAiplatformV1beta1PredefinedSplit
Supported only for tabular Datasets. Split based on a predefined key.
savedQueryId string
Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
stratifiedSplit GoogleCloudAiplatformV1beta1StratifiedSplit
Supported only for tabular Datasets. Split based on the distribution of the specified column.
timestampSplit GoogleCloudAiplatformV1beta1TimestampSplit
Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
dataset_id This property is required. str
The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
annotation_schema_uri str
Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
annotations_filter str
Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
bigquery_destination GoogleCloudAiplatformV1beta1BigQueryDestination
Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset___ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. * AIP_DATA_FORMAT = "bigquery". * AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" * AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" * AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"
filter_split GoogleCloudAiplatformV1beta1FilterSplit
Split based on the provided filters for each set.
fraction_split GoogleCloudAiplatformV1beta1FractionSplit
Split based on fractions defining the size of each set.
gcs_destination GoogleCloudAiplatformV1beta1GcsDestination
The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset--- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-.jsonl" * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-.${AIP_DATA_FORMAT}" * AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-.${AIP_DATA_FORMAT}" * AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-.${AIP_DATA_FORMAT}"
persist_ml_use_assignment bool
Whether to persist the ML use assignment to data item system labels.
predefined_split GoogleCloudAiplatformV1beta1PredefinedSplit
Supported only for tabular Datasets. Split based on a predefined key.
saved_query_id str
Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
stratified_split GoogleCloudAiplatformV1beta1StratifiedSplit
Supported only for tabular Datasets. Split based on the distribution of the specified column.
timestamp_split GoogleCloudAiplatformV1beta1TimestampSplit
Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
datasetId This property is required. String
The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
annotationSchemaUri String
Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
annotationsFilter String
Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
bigqueryDestination Property Map
Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset___ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. * AIP_DATA_FORMAT = "bigquery". * AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" * AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" * AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"
filterSplit Property Map
Split based on the provided filters for each set.
fractionSplit Property Map
Split based on fractions defining the size of each set.
gcsDestination Property Map
The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset--- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-.jsonl" * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-.${AIP_DATA_FORMAT}" * AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-.${AIP_DATA_FORMAT}" * AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-.${AIP_DATA_FORMAT}"
persistMlUseAssignment Boolean
Whether to persist the ML use assignment to data item system labels.
predefinedSplit Property Map
Supported only for tabular Datasets. Split based on a predefined key.
savedQueryId String
Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
stratifiedSplit Property Map
Supported only for tabular Datasets. Split based on the distribution of the specified column.
timestampSplit Property Map
Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.

GoogleCloudAiplatformV1beta1InputDataConfigResponse
, GoogleCloudAiplatformV1beta1InputDataConfigResponseArgs

AnnotationSchemaUri This property is required. string
Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
AnnotationsFilter This property is required. string
Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
BigqueryDestination This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset___ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. * AIP_DATA_FORMAT = "bigquery". * AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" * AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" * AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"
DatasetId This property is required. string
The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
FilterSplit This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FilterSplitResponse
Split based on the provided filters for each set.
FractionSplit This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FractionSplitResponse
Split based on fractions defining the size of each set.
GcsDestination This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationResponse
The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset--- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-.jsonl" * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-.${AIP_DATA_FORMAT}" * AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-.${AIP_DATA_FORMAT}" * AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-.${AIP_DATA_FORMAT}"
PersistMlUseAssignment This property is required. bool
Whether to persist the ML use assignment to data item system labels.
PredefinedSplit This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PredefinedSplitResponse
Supported only for tabular Datasets. Split based on a predefined key.
SavedQueryId This property is required. string
Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
StratifiedSplit This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StratifiedSplitResponse
Supported only for tabular Datasets. Split based on the distribution of the specified column.
TimestampSplit This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1TimestampSplitResponse
Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
AnnotationSchemaUri This property is required. string
Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
AnnotationsFilter This property is required. string
Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
BigqueryDestination This property is required. GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset___ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. * AIP_DATA_FORMAT = "bigquery". * AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" * AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" * AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"
DatasetId This property is required. string
The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
FilterSplit This property is required. GoogleCloudAiplatformV1beta1FilterSplitResponse
Split based on the provided filters for each set.
FractionSplit This property is required. GoogleCloudAiplatformV1beta1FractionSplitResponse
Split based on fractions defining the size of each set.
GcsDestination This property is required. GoogleCloudAiplatformV1beta1GcsDestinationResponse
The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset--- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-.jsonl" * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-.${AIP_DATA_FORMAT}" * AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-.${AIP_DATA_FORMAT}" * AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-.${AIP_DATA_FORMAT}"
PersistMlUseAssignment This property is required. bool
Whether to persist the ML use assignment to data item system labels.
PredefinedSplit This property is required. GoogleCloudAiplatformV1beta1PredefinedSplitResponse
Supported only for tabular Datasets. Split based on a predefined key.
SavedQueryId This property is required. string
Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
StratifiedSplit This property is required. GoogleCloudAiplatformV1beta1StratifiedSplitResponse
Supported only for tabular Datasets. Split based on the distribution of the specified column.
TimestampSplit This property is required. GoogleCloudAiplatformV1beta1TimestampSplitResponse
Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
annotationSchemaUri This property is required. String
Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
annotationsFilter This property is required. String
Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
bigqueryDestination This property is required. GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset___ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. * AIP_DATA_FORMAT = "bigquery". * AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" * AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" * AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"
datasetId This property is required. String
The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
filterSplit This property is required. GoogleCloudAiplatformV1beta1FilterSplitResponse
Split based on the provided filters for each set.
fractionSplit This property is required. GoogleCloudAiplatformV1beta1FractionSplitResponse
Split based on fractions defining the size of each set.
gcsDestination This property is required. GoogleCloudAiplatformV1beta1GcsDestinationResponse
The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset--- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-.jsonl" * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-.${AIP_DATA_FORMAT}" * AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-.${AIP_DATA_FORMAT}" * AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-.${AIP_DATA_FORMAT}"
persistMlUseAssignment This property is required. Boolean
Whether to persist the ML use assignment to data item system labels.
predefinedSplit This property is required. GoogleCloudAiplatformV1beta1PredefinedSplitResponse
Supported only for tabular Datasets. Split based on a predefined key.
savedQueryId This property is required. String
Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
stratifiedSplit This property is required. GoogleCloudAiplatformV1beta1StratifiedSplitResponse
Supported only for tabular Datasets. Split based on the distribution of the specified column.
timestampSplit This property is required. GoogleCloudAiplatformV1beta1TimestampSplitResponse
Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
annotationSchemaUri This property is required. string
Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
annotationsFilter This property is required. string
Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
bigqueryDestination This property is required. GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset___ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. * AIP_DATA_FORMAT = "bigquery". * AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" * AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" * AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"
datasetId This property is required. string
The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
filterSplit This property is required. GoogleCloudAiplatformV1beta1FilterSplitResponse
Split based on the provided filters for each set.
fractionSplit This property is required. GoogleCloudAiplatformV1beta1FractionSplitResponse
Split based on fractions defining the size of each set.
gcsDestination This property is required. GoogleCloudAiplatformV1beta1GcsDestinationResponse
The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset--- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-.jsonl" * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-.${AIP_DATA_FORMAT}" * AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-.${AIP_DATA_FORMAT}" * AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-.${AIP_DATA_FORMAT}"
persistMlUseAssignment This property is required. boolean
Whether to persist the ML use assignment to data item system labels.
predefinedSplit This property is required. GoogleCloudAiplatformV1beta1PredefinedSplitResponse
Supported only for tabular Datasets. Split based on a predefined key.
savedQueryId This property is required. string
Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
stratifiedSplit This property is required. GoogleCloudAiplatformV1beta1StratifiedSplitResponse
Supported only for tabular Datasets. Split based on the distribution of the specified column.
timestampSplit This property is required. GoogleCloudAiplatformV1beta1TimestampSplitResponse
Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
annotation_schema_uri This property is required. str
Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
annotations_filter This property is required. str
Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
bigquery_destination This property is required. GoogleCloudAiplatformV1beta1BigQueryDestinationResponse
Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset___ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. * AIP_DATA_FORMAT = "bigquery". * AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" * AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" * AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"
dataset_id This property is required. str
The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
filter_split This property is required. GoogleCloudAiplatformV1beta1FilterSplitResponse
Split based on the provided filters for each set.
fraction_split This property is required. GoogleCloudAiplatformV1beta1FractionSplitResponse
Split based on fractions defining the size of each set.
gcs_destination This property is required. GoogleCloudAiplatformV1beta1GcsDestinationResponse
The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset--- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-.jsonl" * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-.${AIP_DATA_FORMAT}" * AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-.${AIP_DATA_FORMAT}" * AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-.${AIP_DATA_FORMAT}"
persist_ml_use_assignment This property is required. bool
Whether to persist the ML use assignment to data item system labels.
predefined_split This property is required. GoogleCloudAiplatformV1beta1PredefinedSplitResponse
Supported only for tabular Datasets. Split based on a predefined key.
saved_query_id This property is required. str
Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
stratified_split This property is required. GoogleCloudAiplatformV1beta1StratifiedSplitResponse
Supported only for tabular Datasets. Split based on the distribution of the specified column.
timestamp_split This property is required. GoogleCloudAiplatformV1beta1TimestampSplitResponse
Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
annotationSchemaUri This property is required. String
Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
annotationsFilter This property is required. String
Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Vertex AI). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
bigqueryDestination This property is required. Property Map
Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset___ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. * AIP_DATA_FORMAT = "bigquery". * AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" * AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" * AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"
datasetId This property is required. String
The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
filterSplit This property is required. Property Map
Split based on the provided filters for each set.
fractionSplit This property is required. Property Map
Split based on fractions defining the size of each set.
gcsDestination This property is required. Property Map
The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset--- where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Vertex AI environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-.jsonl" * AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data * AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-.${AIP_DATA_FORMAT}" * AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-.${AIP_DATA_FORMAT}" * AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-.${AIP_DATA_FORMAT}"
persistMlUseAssignment This property is required. Boolean
Whether to persist the ML use assignment to data item system labels.
predefinedSplit This property is required. Property Map
Supported only for tabular Datasets. Split based on a predefined key.
savedQueryId This property is required. String
Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
stratifiedSplit This property is required. Property Map
Supported only for tabular Datasets. Split based on the distribution of the specified column.
timestampSplit This property is required. Property Map
Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.

GoogleCloudAiplatformV1beta1IntegratedGradientsAttribution
, GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionArgs

StepCount This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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.

GoogleCloudAiplatformV1beta1Model
, GoogleCloudAiplatformV1beta1ModelArgs

DisplayName This property is required. string
The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
ArtifactUri string
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models or Large Models.
ContainerSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelContainerSpec
Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models or Large Models.
Description string
The description of the Model.
EncryptionSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EncryptionSpec
Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
Etag string
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
ExplanationSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationSpec
The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
Labels Dictionary<string, string>
The labels with user-defined metadata to organize your Models. 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.
Metadata object
Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
MetadataSchemaUri string
Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field 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.
Name string
The resource name of the Model.
PredictSchemata Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PredictSchemata
The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
VersionAliases List<string>
User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
VersionDescription string
The description of this version.
DisplayName This property is required. string
The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
ArtifactUri string
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models or Large Models.
ContainerSpec GoogleCloudAiplatformV1beta1ModelContainerSpec
Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models or Large Models.
Description string
The description of the Model.
EncryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpec
Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
Etag string
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
ExplanationSpec GoogleCloudAiplatformV1beta1ExplanationSpec
The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
Labels map[string]string
The labels with user-defined metadata to organize your Models. 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.
Metadata interface{}
Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
MetadataSchemaUri string
Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field 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.
Name string
The resource name of the Model.
PredictSchemata GoogleCloudAiplatformV1beta1PredictSchemata
The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
VersionAliases []string
User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
VersionDescription string
The description of this version.
displayName This property is required. String
The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
artifactUri String
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models or Large Models.
containerSpec GoogleCloudAiplatformV1beta1ModelContainerSpec
Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models or Large Models.
description String
The description of the Model.
encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpec
Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
etag String
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
explanationSpec GoogleCloudAiplatformV1beta1ExplanationSpec
The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
labels Map<String,String>
The labels with user-defined metadata to organize your Models. 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.
metadata Object
Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
metadataSchemaUri String
Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field 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.
name String
The resource name of the Model.
predictSchemata GoogleCloudAiplatformV1beta1PredictSchemata
The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
versionAliases List<String>
User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
versionDescription String
The description of this version.
displayName This property is required. string
The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
artifactUri string
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models or Large Models.
containerSpec GoogleCloudAiplatformV1beta1ModelContainerSpec
Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models or Large Models.
description string
The description of the Model.
encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpec
Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
etag string
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
explanationSpec GoogleCloudAiplatformV1beta1ExplanationSpec
The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
labels {[key: string]: string}
The labels with user-defined metadata to organize your Models. 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.
metadata any
Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
metadataSchemaUri string
Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field 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.
name string
The resource name of the Model.
predictSchemata GoogleCloudAiplatformV1beta1PredictSchemata
The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
versionAliases string[]
User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
versionDescription string
The description of this version.
display_name This property is required. str
The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
artifact_uri str
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models or Large Models.
container_spec GoogleCloudAiplatformV1beta1ModelContainerSpec
Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models or Large Models.
description str
The description of the Model.
encryption_spec GoogleCloudAiplatformV1beta1EncryptionSpec
Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
etag str
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
explanation_spec GoogleCloudAiplatformV1beta1ExplanationSpec
The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
labels Mapping[str, str]
The labels with user-defined metadata to organize your Models. 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.
metadata Any
Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
metadata_schema_uri str
Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field 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.
name str
The resource name of the Model.
predict_schemata GoogleCloudAiplatformV1beta1PredictSchemata
The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
version_aliases Sequence[str]
User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
version_description str
The description of this version.
displayName This property is required. String
The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
artifactUri String
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models or Large Models.
containerSpec Property Map
Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models or Large Models.
description String
The description of the Model.
encryptionSpec Property Map
Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
etag String
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
explanationSpec Property Map
The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
labels Map<String>
The labels with user-defined metadata to organize your Models. 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.
metadata Any
Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
metadataSchemaUri String
Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field 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.
name String
The resource name of the Model.
predictSchemata Property Map
The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
versionAliases List<String>
User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
versionDescription String
The description of this version.

GoogleCloudAiplatformV1beta1ModelContainerSpec
, GoogleCloudAiplatformV1beta1ModelContainerSpecArgs

ImageUri This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. string
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
Env This property is required. 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 This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeResponse
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
HealthRoute This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeResponse
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
Args This property is required. []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 This property is required. []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 This property is required. string
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
Env This property is required. []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 This property is required. GoogleCloudAiplatformV1beta1ProbeResponse
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
HealthRoute This property is required. 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 This property is required. 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 This property is required. []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 This property is required. 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 This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ProbeResponse
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
args This property is required. 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 This property is required. 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 This property is required. String
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ProbeResponse
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
healthRoute This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ProbeResponse
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
args This property is required. 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 This property is required. 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 This property is required. string
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ProbeResponse
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
healthRoute This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ProbeResponse
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
args This property is required. 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 This property is required. 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 This property is required. str
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ProbeResponse
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
health_route This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ProbeResponse
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
args This property is required. 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 This property is required. 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 This property is required. String
Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
env This property is required. 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 This property is required. Property Map
Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
healthRoute This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. Property Map
Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.

GoogleCloudAiplatformV1beta1ModelExportFormatResponse
, GoogleCloudAiplatformV1beta1ModelExportFormatResponseArgs

ExportableContents This property is required. List<string>
The content of this Model that may be exported.
ExportableContents This property is required. []string
The content of this Model that may be exported.
exportableContents This property is required. List<String>
The content of this Model that may be exported.
exportableContents This property is required. string[]
The content of this Model that may be exported.
exportable_contents This property is required. Sequence[str]
The content of this Model that may be exported.
exportableContents This property is required. List<String>
The content of this Model that may be exported.

GoogleCloudAiplatformV1beta1ModelOriginalModelInfoResponse
, GoogleCloudAiplatformV1beta1ModelOriginalModelInfoResponseArgs

Model This property is required. string
The resource name of the Model this Model is a copy of, including the revision. Format: projects/{project}/locations/{location}/models/{model_id}@{version_id}
Model This property is required. string
The resource name of the Model this Model is a copy of, including the revision. Format: projects/{project}/locations/{location}/models/{model_id}@{version_id}
model This property is required. String
The resource name of the Model this Model is a copy of, including the revision. Format: projects/{project}/locations/{location}/models/{model_id}@{version_id}
model This property is required. string
The resource name of the Model this Model is a copy of, including the revision. Format: projects/{project}/locations/{location}/models/{model_id}@{version_id}
model This property is required. str
The resource name of the Model this Model is a copy of, including the revision. Format: projects/{project}/locations/{location}/models/{model_id}@{version_id}
model This property is required. String
The resource name of the Model this Model is a copy of, including the revision. Format: projects/{project}/locations/{location}/models/{model_id}@{version_id}

GoogleCloudAiplatformV1beta1ModelResponse
, GoogleCloudAiplatformV1beta1ModelResponseArgs

ArtifactUri This property is required. string
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models or Large Models.
ContainerSpec This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelContainerSpecResponse
Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models or Large Models.
CreateTime This property is required. string
Timestamp when this Model was uploaded into Vertex AI.
DeployedModels This property is required. List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1DeployedModelRefResponse>
The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
Description This property is required. string
The description of the Model.
DisplayName This property is required. string
The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
EncryptionSpec This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EncryptionSpecResponse
Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
Etag This property is required. string
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
ExplanationSpec This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationSpecResponse
The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
Labels This property is required. Dictionary<string, string>
The labels with user-defined metadata to organize your Models. 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.
Metadata This property is required. object
Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
MetadataArtifact This property is required. string
The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.
MetadataSchemaUri This property is required. string
Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field 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.
ModelSourceInfo This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelSourceInfoResponse
Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
Name This property is required. string
The resource name of the Model.
OriginalModelInfo This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelOriginalModelInfoResponse
If this Model is a copy of another Model, this contains info about the original.
PredictSchemata This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PredictSchemataResponse
The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
SupportedDeploymentResourcesTypes This property is required. List<string>
When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.
SupportedExportFormats This property is required. List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelExportFormatResponse>
The formats in which this Model may be exported. If empty, this Model is not available for export.
SupportedInputStorageFormats This property is required. List<string>
The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema. The possible formats are: * jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource. * csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource. * tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. * tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource. * bigquery Each instance is a single row in BigQuery. Uses BigQuerySource. * file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
SupportedOutputStorageFormats This property is required. List<string>
The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema). The possible formats are: * jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination. * csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination. * bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
TrainingPipeline This property is required. string
The resource name of the TrainingPipeline that uploaded this Model, if any.
UpdateTime This property is required. string
Timestamp when this Model was most recently updated.
VersionAliases This property is required. List<string>
User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
VersionCreateTime This property is required. string
Timestamp when this version was created.
VersionDescription This property is required. string
The description of this version.
VersionId This property is required. string
Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.
VersionUpdateTime This property is required. string
Timestamp when this version was most recently updated.
ArtifactUri This property is required. string
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models or Large Models.
ContainerSpec This property is required. GoogleCloudAiplatformV1beta1ModelContainerSpecResponse
Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models or Large Models.
CreateTime This property is required. string
Timestamp when this Model was uploaded into Vertex AI.
DeployedModels This property is required. []GoogleCloudAiplatformV1beta1DeployedModelRefResponse
The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
Description This property is required. string
The description of the Model.
DisplayName This property is required. string
The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
EncryptionSpec This property is required. GoogleCloudAiplatformV1beta1EncryptionSpecResponse
Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
Etag This property is required. string
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
ExplanationSpec This property is required. GoogleCloudAiplatformV1beta1ExplanationSpecResponse
The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
Labels This property is required. map[string]string
The labels with user-defined metadata to organize your Models. 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.
Metadata This property is required. interface{}
Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
MetadataArtifact This property is required. string
The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.
MetadataSchemaUri This property is required. string
Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field 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.
ModelSourceInfo This property is required. GoogleCloudAiplatformV1beta1ModelSourceInfoResponse
Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
Name This property is required. string
The resource name of the Model.
OriginalModelInfo This property is required. GoogleCloudAiplatformV1beta1ModelOriginalModelInfoResponse
If this Model is a copy of another Model, this contains info about the original.
PredictSchemata This property is required. GoogleCloudAiplatformV1beta1PredictSchemataResponse
The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
SupportedDeploymentResourcesTypes This property is required. []string
When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.
SupportedExportFormats This property is required. []GoogleCloudAiplatformV1beta1ModelExportFormatResponse
The formats in which this Model may be exported. If empty, this Model is not available for export.
SupportedInputStorageFormats This property is required. []string
The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema. The possible formats are: * jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource. * csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource. * tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. * tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource. * bigquery Each instance is a single row in BigQuery. Uses BigQuerySource. * file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
SupportedOutputStorageFormats This property is required. []string
The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema). The possible formats are: * jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination. * csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination. * bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
TrainingPipeline This property is required. string
The resource name of the TrainingPipeline that uploaded this Model, if any.
UpdateTime This property is required. string
Timestamp when this Model was most recently updated.
VersionAliases This property is required. []string
User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
VersionCreateTime This property is required. string
Timestamp when this version was created.
VersionDescription This property is required. string
The description of this version.
VersionId This property is required. string
Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.
VersionUpdateTime This property is required. string
Timestamp when this version was most recently updated.
artifactUri This property is required. String
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models or Large Models.
containerSpec This property is required. GoogleCloudAiplatformV1beta1ModelContainerSpecResponse
Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models or Large Models.
createTime This property is required. String
Timestamp when this Model was uploaded into Vertex AI.
deployedModels This property is required. List<GoogleCloudAiplatformV1beta1DeployedModelRefResponse>
The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
description This property is required. String
The description of the Model.
displayName This property is required. String
The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryptionSpec This property is required. GoogleCloudAiplatformV1beta1EncryptionSpecResponse
Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
etag This property is required. String
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
explanationSpec This property is required. GoogleCloudAiplatformV1beta1ExplanationSpecResponse
The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
labels This property is required. Map<String,String>
The labels with user-defined metadata to organize your Models. 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.
metadata This property is required. Object
Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
metadataArtifact This property is required. String
The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.
metadataSchemaUri This property is required. String
Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field 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.
modelSourceInfo This property is required. GoogleCloudAiplatformV1beta1ModelSourceInfoResponse
Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
name This property is required. String
The resource name of the Model.
originalModelInfo This property is required. GoogleCloudAiplatformV1beta1ModelOriginalModelInfoResponse
If this Model is a copy of another Model, this contains info about the original.
predictSchemata This property is required. GoogleCloudAiplatformV1beta1PredictSchemataResponse
The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
supportedDeploymentResourcesTypes This property is required. List<String>
When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.
supportedExportFormats This property is required. List<GoogleCloudAiplatformV1beta1ModelExportFormatResponse>
The formats in which this Model may be exported. If empty, this Model is not available for export.
supportedInputStorageFormats This property is required. List<String>
The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema. The possible formats are: * jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource. * csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource. * tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. * tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource. * bigquery Each instance is a single row in BigQuery. Uses BigQuerySource. * file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
supportedOutputStorageFormats This property is required. List<String>
The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema). The possible formats are: * jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination. * csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination. * bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
trainingPipeline This property is required. String
The resource name of the TrainingPipeline that uploaded this Model, if any.
updateTime This property is required. String
Timestamp when this Model was most recently updated.
versionAliases This property is required. List<String>
User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
versionCreateTime This property is required. String
Timestamp when this version was created.
versionDescription This property is required. String
The description of this version.
versionId This property is required. String
Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.
versionUpdateTime This property is required. String
Timestamp when this version was most recently updated.
artifactUri This property is required. string
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models or Large Models.
containerSpec This property is required. GoogleCloudAiplatformV1beta1ModelContainerSpecResponse
Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models or Large Models.
createTime This property is required. string
Timestamp when this Model was uploaded into Vertex AI.
deployedModels This property is required. GoogleCloudAiplatformV1beta1DeployedModelRefResponse[]
The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
description This property is required. string
The description of the Model.
displayName This property is required. string
The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryptionSpec This property is required. GoogleCloudAiplatformV1beta1EncryptionSpecResponse
Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
etag This property is required. string
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
explanationSpec This property is required. GoogleCloudAiplatformV1beta1ExplanationSpecResponse
The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
labels This property is required. {[key: string]: string}
The labels with user-defined metadata to organize your Models. 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.
metadata This property is required. any
Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
metadataArtifact This property is required. string
The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.
metadataSchemaUri This property is required. string
Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field 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.
modelSourceInfo This property is required. GoogleCloudAiplatformV1beta1ModelSourceInfoResponse
Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
name This property is required. string
The resource name of the Model.
originalModelInfo This property is required. GoogleCloudAiplatformV1beta1ModelOriginalModelInfoResponse
If this Model is a copy of another Model, this contains info about the original.
predictSchemata This property is required. GoogleCloudAiplatformV1beta1PredictSchemataResponse
The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
supportedDeploymentResourcesTypes This property is required. string[]
When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.
supportedExportFormats This property is required. GoogleCloudAiplatformV1beta1ModelExportFormatResponse[]
The formats in which this Model may be exported. If empty, this Model is not available for export.
supportedInputStorageFormats This property is required. string[]
The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema. The possible formats are: * jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource. * csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource. * tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. * tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource. * bigquery Each instance is a single row in BigQuery. Uses BigQuerySource. * file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
supportedOutputStorageFormats This property is required. string[]
The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema). The possible formats are: * jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination. * csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination. * bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
trainingPipeline This property is required. string
The resource name of the TrainingPipeline that uploaded this Model, if any.
updateTime This property is required. string
Timestamp when this Model was most recently updated.
versionAliases This property is required. string[]
User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
versionCreateTime This property is required. string
Timestamp when this version was created.
versionDescription This property is required. string
The description of this version.
versionId This property is required. string
Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.
versionUpdateTime This property is required. string
Timestamp when this version was most recently updated.
artifact_uri This property is required. str
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models or Large Models.
container_spec This property is required. GoogleCloudAiplatformV1beta1ModelContainerSpecResponse
Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models or Large Models.
create_time This property is required. str
Timestamp when this Model was uploaded into Vertex AI.
deployed_models This property is required. Sequence[GoogleCloudAiplatformV1beta1DeployedModelRefResponse]
The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
description This property is required. str
The description of the Model.
display_name This property is required. str
The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryption_spec This property is required. GoogleCloudAiplatformV1beta1EncryptionSpecResponse
Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
etag This property is required. str
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
explanation_spec This property is required. GoogleCloudAiplatformV1beta1ExplanationSpecResponse
The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
labels This property is required. Mapping[str, str]
The labels with user-defined metadata to organize your Models. 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.
metadata This property is required. Any
Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
metadata_artifact This property is required. str
The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.
metadata_schema_uri This property is required. str
Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field 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.
model_source_info This property is required. GoogleCloudAiplatformV1beta1ModelSourceInfoResponse
Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
name This property is required. str
The resource name of the Model.
original_model_info This property is required. GoogleCloudAiplatformV1beta1ModelOriginalModelInfoResponse
If this Model is a copy of another Model, this contains info about the original.
predict_schemata This property is required. GoogleCloudAiplatformV1beta1PredictSchemataResponse
The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
supported_deployment_resources_types This property is required. Sequence[str]
When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.
supported_export_formats This property is required. Sequence[GoogleCloudAiplatformV1beta1ModelExportFormatResponse]
The formats in which this Model may be exported. If empty, this Model is not available for export.
supported_input_storage_formats This property is required. Sequence[str]
The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema. The possible formats are: * jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource. * csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource. * tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. * tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource. * bigquery Each instance is a single row in BigQuery. Uses BigQuerySource. * file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
supported_output_storage_formats This property is required. Sequence[str]
The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema). The possible formats are: * jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination. * csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination. * bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
training_pipeline This property is required. str
The resource name of the TrainingPipeline that uploaded this Model, if any.
update_time This property is required. str
Timestamp when this Model was most recently updated.
version_aliases This property is required. Sequence[str]
User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
version_create_time This property is required. str
Timestamp when this version was created.
version_description This property is required. str
The description of this version.
version_id This property is required. str
Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.
version_update_time This property is required. str
Timestamp when this version was most recently updated.
artifactUri This property is required. String
Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models or Large Models.
containerSpec This property is required. Property Map
Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Vertex AI. Not present for AutoML Models or Large Models.
createTime This property is required. String
Timestamp when this Model was uploaded into Vertex AI.
deployedModels This property is required. List<Property Map>
The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
description This property is required. String
The description of the Model.
displayName This property is required. String
The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
encryptionSpec This property is required. Property Map
Customer-managed encryption key spec for a Model. If set, this Model and all sub-resources of this Model will be secured by this key.
etag This property is required. String
Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
explanationSpec This property is required. Property Map
The default explanation specification for this Model. The Model can be used for requesting explanation after being deployed if it is populated. The Model can be used for batch explanation if it is populated. All fields of the explanation_spec can be overridden by explanation_spec of DeployModelRequest.deployed_model, or explanation_spec of BatchPredictionJob. If the default explanation specification is not set for this Model, this Model can still be used for requesting explanation by setting explanation_spec of DeployModelRequest.deployed_model and for batch explanation by setting explanation_spec of BatchPredictionJob.
labels This property is required. Map<String>
The labels with user-defined metadata to organize your Models. 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.
metadata This property is required. Any
Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
metadataArtifact This property is required. String
The resource name of the Artifact that was created in MetadataStore when creating the Model. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.
metadataSchemaUri This property is required. String
Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field 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.
modelSourceInfo This property is required. Property Map
Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
name This property is required. String
The resource name of the Model.
originalModelInfo This property is required. Property Map
If this Model is a copy of another Model, this contains info about the original.
predictSchemata This property is required. Property Map
The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
supportedDeploymentResourcesTypes This property is required. List<String>
When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.
supportedExportFormats This property is required. List<Property Map>
The formats in which this Model may be exported. If empty, this Model is not available for export.
supportedInputStorageFormats This property is required. List<String>
The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema. The possible formats are: * jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource. * csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource. * tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. * tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource. * bigquery Each instance is a single row in BigQuery. Uses BigQuerySource. * file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
supportedOutputStorageFormats This property is required. List<String>
The formats this Model supports in BatchPredictionJob.output_config. If both PredictSchemata.instance_schema_uri and PredictSchemata.prediction_schema_uri exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema). The possible formats are: * jsonl The JSON Lines format, where each prediction is a single line. Uses GcsDestination. * csv The CSV format, where each prediction is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsDestination. * bigquery Each prediction is a single row in a BigQuery table, uses BigQueryDestination . If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
trainingPipeline This property is required. String
The resource name of the TrainingPipeline that uploaded this Model, if any.
updateTime This property is required. String
Timestamp when this Model was most recently updated.
versionAliases This property is required. List<String>
User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is a-z{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
versionCreateTime This property is required. String
Timestamp when this version was created.
versionDescription This property is required. String
The description of this version.
versionId This property is required. String
Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.
versionUpdateTime This property is required. String
Timestamp when this version was most recently updated.

GoogleCloudAiplatformV1beta1ModelSourceInfoResponse
, GoogleCloudAiplatformV1beta1ModelSourceInfoResponseArgs

Copy This property is required. bool
If this Model is copy of another Model. If true then source_type pertains to the original.
SourceType This property is required. string
Type of the model source.
Copy This property is required. bool
If this Model is copy of another Model. If true then source_type pertains to the original.
SourceType This property is required. string
Type of the model source.
copy This property is required. Boolean
If this Model is copy of another Model. If true then source_type pertains to the original.
sourceType This property is required. String
Type of the model source.
copy This property is required. boolean
If this Model is copy of another Model. If true then source_type pertains to the original.
sourceType This property is required. string
Type of the model source.
copy This property is required. bool
If this Model is copy of another Model. If true then source_type pertains to the original.
source_type This property is required. str
Type of the model source.
copy This property is required. Boolean
If this Model is copy of another Model. If true then source_type pertains to the original.
sourceType This property is required. String
Type of the model source.

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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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.

GoogleCloudAiplatformV1beta1PredefinedSplit
, GoogleCloudAiplatformV1beta1PredefinedSplitArgs

Key This property is required. string
The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
Key This property is required. string
The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
key This property is required. String
The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
key This property is required. string
The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
key This property is required. str
The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
key This property is required. String
The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.

GoogleCloudAiplatformV1beta1PredefinedSplitResponse
, GoogleCloudAiplatformV1beta1PredefinedSplitResponseArgs

Key This property is required. string
The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
Key This property is required. string
The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
key This property is required. String
The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
key This property is required. string
The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
key This property is required. str
The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
key This property is required. String
The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.

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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. string
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
Modality This property is required. 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 This property is required. string
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
modality This property is required. 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 This property is required. String
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
modality This property is required. 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 This property is required. string
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
modality This property is required. 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 This property is required. str
Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
modality This property is required. 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 This property is required. 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 This property is required. 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 This property is required. []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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeExecActionResponse
Exec specifies the action to take.
PeriodSeconds This property is required. 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 This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ProbeExecActionResponse
Exec specifies the action to take.
PeriodSeconds This property is required. 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 This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ProbeExecActionResponse
Exec specifies the action to take.
periodSeconds This property is required. 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 This property is required. 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 This property is required. GoogleCloudAiplatformV1beta1ProbeExecActionResponse
Exec specifies the action to take.
periodSeconds This property is required. 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 This property is required. 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_ This property is required. GoogleCloudAiplatformV1beta1ProbeExecActionResponse
Exec specifies the action to take.
period_seconds This property is required. 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 This property is required. 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 This property is required. Property Map
Exec specifies the action to take.
periodSeconds This property is required. 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 This property is required. 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'.

GoogleCloudAiplatformV1beta1SampledShapleyAttribution
, GoogleCloudAiplatformV1beta1SampledShapleyAttributionArgs

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

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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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.

GoogleCloudAiplatformV1beta1StratifiedSplit
, GoogleCloudAiplatformV1beta1StratifiedSplitArgs

Key This property is required. string
The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
TestFraction double
The fraction of the input data that is to be used to evaluate the Model.
TrainingFraction double
The fraction of the input data that is to be used to train the Model.
ValidationFraction double
The fraction of the input data that is to be used to validate the Model.
Key This property is required. string
The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
TestFraction float64
The fraction of the input data that is to be used to evaluate the Model.
TrainingFraction float64
The fraction of the input data that is to be used to train the Model.
ValidationFraction float64
The fraction of the input data that is to be used to validate the Model.
key This property is required. String
The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
testFraction Double
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction Double
The fraction of the input data that is to be used to train the Model.
validationFraction Double
The fraction of the input data that is to be used to validate the Model.
key This property is required. string
The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
testFraction number
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction number
The fraction of the input data that is to be used to train the Model.
validationFraction number
The fraction of the input data that is to be used to validate the Model.
key This property is required. str
The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
test_fraction float
The fraction of the input data that is to be used to evaluate the Model.
training_fraction float
The fraction of the input data that is to be used to train the Model.
validation_fraction float
The fraction of the input data that is to be used to validate the Model.
key This property is required. String
The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
testFraction Number
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction Number
The fraction of the input data that is to be used to train the Model.
validationFraction Number
The fraction of the input data that is to be used to validate the Model.

GoogleCloudAiplatformV1beta1StratifiedSplitResponse
, GoogleCloudAiplatformV1beta1StratifiedSplitResponseArgs

Key This property is required. string
The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
TestFraction This property is required. double
The fraction of the input data that is to be used to evaluate the Model.
TrainingFraction This property is required. double
The fraction of the input data that is to be used to train the Model.
ValidationFraction This property is required. double
The fraction of the input data that is to be used to validate the Model.
Key This property is required. string
The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
TestFraction This property is required. float64
The fraction of the input data that is to be used to evaluate the Model.
TrainingFraction This property is required. float64
The fraction of the input data that is to be used to train the Model.
ValidationFraction This property is required. float64
The fraction of the input data that is to be used to validate the Model.
key This property is required. String
The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
testFraction This property is required. Double
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction This property is required. Double
The fraction of the input data that is to be used to train the Model.
validationFraction This property is required. Double
The fraction of the input data that is to be used to validate the Model.
key This property is required. string
The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
testFraction This property is required. number
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction This property is required. number
The fraction of the input data that is to be used to train the Model.
validationFraction This property is required. number
The fraction of the input data that is to be used to validate the Model.
key This property is required. str
The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
test_fraction This property is required. float
The fraction of the input data that is to be used to evaluate the Model.
training_fraction This property is required. float
The fraction of the input data that is to be used to train the Model.
validation_fraction This property is required. float
The fraction of the input data that is to be used to validate the Model.
key This property is required. String
The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
testFraction This property is required. Number
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction This property is required. Number
The fraction of the input data that is to be used to train the Model.
validationFraction This property is required. Number
The fraction of the input data that is to be used to validate the Model.

GoogleCloudAiplatformV1beta1TimestampSplit
, GoogleCloudAiplatformV1beta1TimestampSplitArgs

Key This property is required. string
The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
TestFraction double
The fraction of the input data that is to be used to evaluate the Model.
TrainingFraction double
The fraction of the input data that is to be used to train the Model.
ValidationFraction double
The fraction of the input data that is to be used to validate the Model.
Key This property is required. string
The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
TestFraction float64
The fraction of the input data that is to be used to evaluate the Model.
TrainingFraction float64
The fraction of the input data that is to be used to train the Model.
ValidationFraction float64
The fraction of the input data that is to be used to validate the Model.
key This property is required. String
The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
testFraction Double
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction Double
The fraction of the input data that is to be used to train the Model.
validationFraction Double
The fraction of the input data that is to be used to validate the Model.
key This property is required. string
The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
testFraction number
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction number
The fraction of the input data that is to be used to train the Model.
validationFraction number
The fraction of the input data that is to be used to validate the Model.
key This property is required. str
The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
test_fraction float
The fraction of the input data that is to be used to evaluate the Model.
training_fraction float
The fraction of the input data that is to be used to train the Model.
validation_fraction float
The fraction of the input data that is to be used to validate the Model.
key This property is required. String
The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
testFraction Number
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction Number
The fraction of the input data that is to be used to train the Model.
validationFraction Number
The fraction of the input data that is to be used to validate the Model.

GoogleCloudAiplatformV1beta1TimestampSplitResponse
, GoogleCloudAiplatformV1beta1TimestampSplitResponseArgs

Key This property is required. string
The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
TestFraction This property is required. double
The fraction of the input data that is to be used to evaluate the Model.
TrainingFraction This property is required. double
The fraction of the input data that is to be used to train the Model.
ValidationFraction This property is required. double
The fraction of the input data that is to be used to validate the Model.
Key This property is required. string
The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
TestFraction This property is required. float64
The fraction of the input data that is to be used to evaluate the Model.
TrainingFraction This property is required. float64
The fraction of the input data that is to be used to train the Model.
ValidationFraction This property is required. float64
The fraction of the input data that is to be used to validate the Model.
key This property is required. String
The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
testFraction This property is required. Double
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction This property is required. Double
The fraction of the input data that is to be used to train the Model.
validationFraction This property is required. Double
The fraction of the input data that is to be used to validate the Model.
key This property is required. string
The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
testFraction This property is required. number
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction This property is required. number
The fraction of the input data that is to be used to train the Model.
validationFraction This property is required. number
The fraction of the input data that is to be used to validate the Model.
key This property is required. str
The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
test_fraction This property is required. float
The fraction of the input data that is to be used to evaluate the Model.
training_fraction This property is required. float
The fraction of the input data that is to be used to train the Model.
validation_fraction This property is required. float
The fraction of the input data that is to be used to validate the Model.
key This property is required. String
The key is a name of one of the Dataset's data columns. The values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline.
testFraction This property is required. Number
The fraction of the input data that is to be used to evaluate the Model.
trainingFraction This property is required. Number
The fraction of the input data that is to be used to train the Model.
validationFraction This property is required. Number
The fraction of the input data that is to be used to validate the Model.

GoogleCloudAiplatformV1beta1XraiAttribution
, GoogleCloudAiplatformV1beta1XraiAttributionArgs

StepCount This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. 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 This property is required. int
The status code, which should be an enum value of google.rpc.Code.
Details This property is required. 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 This property is required. 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 This property is required. int
The status code, which should be an enum value of google.rpc.Code.
Details This property is required. []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 This property is required. 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 This property is required. Integer
The status code, which should be an enum value of google.rpc.Code.
details This property is required. 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 This property is required. 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 This property is required. number
The status code, which should be an enum value of google.rpc.Code.
details This property is required. {[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 This property is required. 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 This property is required. int
The status code, which should be an enum value of google.rpc.Code.
details This property is required. 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 This property is required. 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 This property is required. Number
The status code, which should be an enum value of google.rpc.Code.
details This property is required. 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 This property is required. 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 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