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

google-native.aiplatform/v1.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[GoogleCloudAiplatformV1EncryptionSpecArgs] = None,
                     input_data_config: Optional[GoogleCloudAiplatformV1InputDataConfigArgs] = None,
                     labels: Optional[Mapping[str, str]] = None,
                     location: Optional[str] = None,
                     model_id: Optional[str] = None,
                     model_to_upload: Optional[GoogleCloudAiplatformV1ModelArgs] = 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/v1:TrainingPipeline
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

    Parameters

    name string
    The unique name of the resource.
    args TrainingPipelineArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    resource_name str
    The unique name of the resource.
    args 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 string
    The unique name of the resource.
    args TrainingPipelineArgs
    The arguments to resource properties.
    opts ResourceOption
    Bag of options to control resource's behavior.
    name string
    The unique name of the resource.
    args TrainingPipelineArgs
    The arguments to resource properties.
    opts CustomResourceOptions
    Bag of options to control resource's behavior.
    name String
    The unique name of the resource.
    args 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 trainingPipelineResource = new GoogleNative.Aiplatform.V1.TrainingPipeline("trainingPipelineResource", new()
{
    DisplayName = "string",
    TrainingTaskDefinition = "string",
    TrainingTaskInputs = "any",
    EncryptionSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EncryptionSpecArgs
    {
        KmsKeyName = "string",
    },
    InputDataConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1InputDataConfigArgs
    {
        DatasetId = "string",
        AnnotationSchemaUri = "string",
        AnnotationsFilter = "string",
        BigqueryDestination = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BigQueryDestinationArgs
        {
            OutputUri = "string",
        },
        FilterSplit = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FilterSplitArgs
        {
            TestFilter = "string",
            TrainingFilter = "string",
            ValidationFilter = "string",
        },
        FractionSplit = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FractionSplitArgs
        {
            TestFraction = 0,
            TrainingFraction = 0,
            ValidationFraction = 0,
        },
        GcsDestination = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsDestinationArgs
        {
            OutputUriPrefix = "string",
        },
        PersistMlUseAssignment = false,
        PredefinedSplit = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PredefinedSplitArgs
        {
            Key = "string",
        },
        SavedQueryId = "string",
        StratifiedSplit = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StratifiedSplitArgs
        {
            Key = "string",
            TestFraction = 0,
            TrainingFraction = 0,
            ValidationFraction = 0,
        },
        TimestampSplit = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1TimestampSplitArgs
        {
            Key = "string",
            TestFraction = 0,
            TrainingFraction = 0,
            ValidationFraction = 0,
        },
    },
    Labels = 
    {
        { "string", "string" },
    },
    Location = "string",
    ModelId = "string",
    ModelToUpload = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ModelArgs
    {
        DisplayName = "string",
        ExplanationSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationSpecArgs
        {
            Parameters = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationParametersArgs
            {
                Examples = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExamplesArgs
                {
                    ExampleGcsSource = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExamplesExampleGcsSourceArgs
                    {
                        DataFormat = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat.DataFormatUnspecified,
                        GcsSource = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsSourceArgs
                        {
                            Uris = new[]
                            {
                                "string",
                            },
                        },
                    },
                    NearestNeighborSearchConfig = "any",
                    NeighborCount = 0,
                    Presets = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PresetsArgs
                    {
                        Modality = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1PresetsModality.ModalityUnspecified,
                        Query = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1PresetsQuery.Precise,
                    },
                },
                IntegratedGradientsAttribution = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1IntegratedGradientsAttributionArgs
                {
                    StepCount = 0,
                    BlurBaselineConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BlurBaselineConfigArgs
                    {
                        MaxBlurSigma = 0,
                    },
                    SmoothGradConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SmoothGradConfigArgs
                    {
                        FeatureNoiseSigma = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FeatureNoiseSigmaArgs
                        {
                            NoiseSigma = new[]
                            {
                                new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs
                                {
                                    Name = "string",
                                    Sigma = 0,
                                },
                            },
                        },
                        NoiseSigma = 0,
                        NoisySampleCount = 0,
                    },
                },
                OutputIndices = new[]
                {
                    "any",
                },
                SampledShapleyAttribution = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SampledShapleyAttributionArgs
                {
                    PathCount = 0,
                },
                TopK = 0,
                XraiAttribution = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1XraiAttributionArgs
                {
                    StepCount = 0,
                    BlurBaselineConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BlurBaselineConfigArgs
                    {
                        MaxBlurSigma = 0,
                    },
                    SmoothGradConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SmoothGradConfigArgs
                    {
                        FeatureNoiseSigma = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FeatureNoiseSigmaArgs
                        {
                            NoiseSigma = new[]
                            {
                                new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs
                                {
                                    Name = "string",
                                    Sigma = 0,
                                },
                            },
                        },
                        NoiseSigma = 0,
                        NoisySampleCount = 0,
                    },
                },
            },
            Metadata = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationMetadataArgs
            {
                Inputs = 
                {
                    { "string", "string" },
                },
                Outputs = 
                {
                    { "string", "string" },
                },
                FeatureAttributionsSchemaUri = "string",
                LatentSpaceSource = "string",
            },
        },
        Metadata = "any",
        ContainerSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ModelContainerSpecArgs
        {
            ImageUri = "string",
            Args = new[]
            {
                "string",
            },
            Command = new[]
            {
                "string",
            },
            DeploymentTimeout = "string",
            Env = new[]
            {
                new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EnvVarArgs
                {
                    Name = "string",
                    Value = "string",
                },
            },
            HealthProbe = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ProbeArgs
            {
                Exec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ProbeExecActionArgs
                {
                    Command = new[]
                    {
                        "string",
                    },
                },
                PeriodSeconds = 0,
                TimeoutSeconds = 0,
            },
            HealthRoute = "string",
            Ports = new[]
            {
                new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PortArgs
                {
                    ContainerPort = 0,
                },
            },
            PredictRoute = "string",
            SharedMemorySizeMb = "string",
            StartupProbe = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ProbeArgs
            {
                Exec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ProbeExecActionArgs
                {
                    Command = new[]
                    {
                        "string",
                    },
                },
                PeriodSeconds = 0,
                TimeoutSeconds = 0,
            },
        },
        EncryptionSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EncryptionSpecArgs
        {
            KmsKeyName = "string",
        },
        Etag = "string",
        ArtifactUri = "string",
        Labels = 
        {
            { "string", "string" },
        },
        Description = "string",
        MetadataSchemaUri = "string",
        Name = "string",
        PipelineJob = "string",
        PredictSchemata = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PredictSchemataArgs
        {
            InstanceSchemaUri = "string",
            ParametersSchemaUri = "string",
            PredictionSchemaUri = "string",
        },
        VersionAliases = new[]
        {
            "string",
        },
        VersionDescription = "string",
    },
    ParentModel = "string",
    Project = "string",
});

    example, err := aiplatform.NewTrainingPipeline(ctx, "trainingPipelineResource", &aiplatform.TrainingPipelineArgs{
DisplayName: pulumi.String("string"),
TrainingTaskDefinition: pulumi.String("string"),
TrainingTaskInputs: pulumi.Any("any"),
EncryptionSpec: &aiplatform.GoogleCloudAiplatformV1EncryptionSpecArgs{
KmsKeyName: pulumi.String("string"),
},
InputDataConfig: &aiplatform.GoogleCloudAiplatformV1InputDataConfigArgs{
DatasetId: pulumi.String("string"),
AnnotationSchemaUri: pulumi.String("string"),
AnnotationsFilter: pulumi.String("string"),
BigqueryDestination: &aiplatform.GoogleCloudAiplatformV1BigQueryDestinationArgs{
OutputUri: pulumi.String("string"),
},
FilterSplit: &aiplatform.GoogleCloudAiplatformV1FilterSplitArgs{
TestFilter: pulumi.String("string"),
TrainingFilter: pulumi.String("string"),
ValidationFilter: pulumi.String("string"),
},
FractionSplit: &aiplatform.GoogleCloudAiplatformV1FractionSplitArgs{
TestFraction: pulumi.Float64(0),
TrainingFraction: pulumi.Float64(0),
ValidationFraction: pulumi.Float64(0),
},
GcsDestination: &aiplatform.GoogleCloudAiplatformV1GcsDestinationArgs{
OutputUriPrefix: pulumi.String("string"),
},
PersistMlUseAssignment: pulumi.Bool(false),
PredefinedSplit: &aiplatform.GoogleCloudAiplatformV1PredefinedSplitArgs{
Key: pulumi.String("string"),
},
SavedQueryId: pulumi.String("string"),
StratifiedSplit: &aiplatform.GoogleCloudAiplatformV1StratifiedSplitArgs{
Key: pulumi.String("string"),
TestFraction: pulumi.Float64(0),
TrainingFraction: pulumi.Float64(0),
ValidationFraction: pulumi.Float64(0),
},
TimestampSplit: &aiplatform.GoogleCloudAiplatformV1TimestampSplitArgs{
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.GoogleCloudAiplatformV1ModelArgs{
DisplayName: pulumi.String("string"),
ExplanationSpec: &aiplatform.GoogleCloudAiplatformV1ExplanationSpecArgs{
Parameters: &aiplatform.GoogleCloudAiplatformV1ExplanationParametersArgs{
Examples: &aiplatform.GoogleCloudAiplatformV1ExamplesArgs{
ExampleGcsSource: &aiplatform.GoogleCloudAiplatformV1ExamplesExampleGcsSourceArgs{
DataFormat: aiplatform.GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormatDataFormatUnspecified,
GcsSource: &aiplatform.GoogleCloudAiplatformV1GcsSourceArgs{
Uris: pulumi.StringArray{
pulumi.String("string"),
},
},
},
NearestNeighborSearchConfig: pulumi.Any("any"),
NeighborCount: pulumi.Int(0),
Presets: &aiplatform.GoogleCloudAiplatformV1PresetsArgs{
Modality: aiplatform.GoogleCloudAiplatformV1PresetsModalityModalityUnspecified,
Query: aiplatform.GoogleCloudAiplatformV1PresetsQueryPrecise,
},
},
IntegratedGradientsAttribution: &aiplatform.GoogleCloudAiplatformV1IntegratedGradientsAttributionArgs{
StepCount: pulumi.Int(0),
BlurBaselineConfig: &aiplatform.GoogleCloudAiplatformV1BlurBaselineConfigArgs{
MaxBlurSigma: pulumi.Float64(0),
},
SmoothGradConfig: &aiplatform.GoogleCloudAiplatformV1SmoothGradConfigArgs{
FeatureNoiseSigma: &aiplatform.GoogleCloudAiplatformV1FeatureNoiseSigmaArgs{
NoiseSigma: aiplatform.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArray{
&aiplatform.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs{
Name: pulumi.String("string"),
Sigma: pulumi.Float64(0),
},
},
},
NoiseSigma: pulumi.Float64(0),
NoisySampleCount: pulumi.Int(0),
},
},
OutputIndices: pulumi.Array{
pulumi.Any("any"),
},
SampledShapleyAttribution: &aiplatform.GoogleCloudAiplatformV1SampledShapleyAttributionArgs{
PathCount: pulumi.Int(0),
},
TopK: pulumi.Int(0),
XraiAttribution: &aiplatform.GoogleCloudAiplatformV1XraiAttributionArgs{
StepCount: pulumi.Int(0),
BlurBaselineConfig: &aiplatform.GoogleCloudAiplatformV1BlurBaselineConfigArgs{
MaxBlurSigma: pulumi.Float64(0),
},
SmoothGradConfig: &aiplatform.GoogleCloudAiplatformV1SmoothGradConfigArgs{
FeatureNoiseSigma: &aiplatform.GoogleCloudAiplatformV1FeatureNoiseSigmaArgs{
NoiseSigma: aiplatform.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArray{
&aiplatform.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs{
Name: pulumi.String("string"),
Sigma: pulumi.Float64(0),
},
},
},
NoiseSigma: pulumi.Float64(0),
NoisySampleCount: pulumi.Int(0),
},
},
},
Metadata: &aiplatform.GoogleCloudAiplatformV1ExplanationMetadataArgs{
Inputs: pulumi.StringMap{
"string": pulumi.String("string"),
},
Outputs: pulumi.StringMap{
"string": pulumi.String("string"),
},
FeatureAttributionsSchemaUri: pulumi.String("string"),
LatentSpaceSource: pulumi.String("string"),
},
},
Metadata: pulumi.Any("any"),
ContainerSpec: &aiplatform.GoogleCloudAiplatformV1ModelContainerSpecArgs{
ImageUri: pulumi.String("string"),
Args: pulumi.StringArray{
pulumi.String("string"),
},
Command: pulumi.StringArray{
pulumi.String("string"),
},
DeploymentTimeout: pulumi.String("string"),
Env: aiplatform.GoogleCloudAiplatformV1EnvVarArray{
&aiplatform.GoogleCloudAiplatformV1EnvVarArgs{
Name: pulumi.String("string"),
Value: pulumi.String("string"),
},
},
HealthProbe: &aiplatform.GoogleCloudAiplatformV1ProbeArgs{
Exec: &aiplatform.GoogleCloudAiplatformV1ProbeExecActionArgs{
Command: pulumi.StringArray{
pulumi.String("string"),
},
},
PeriodSeconds: pulumi.Int(0),
TimeoutSeconds: pulumi.Int(0),
},
HealthRoute: pulumi.String("string"),
Ports: aiplatform.GoogleCloudAiplatformV1PortArray{
&aiplatform.GoogleCloudAiplatformV1PortArgs{
ContainerPort: pulumi.Int(0),
},
},
PredictRoute: pulumi.String("string"),
SharedMemorySizeMb: pulumi.String("string"),
StartupProbe: &aiplatform.GoogleCloudAiplatformV1ProbeArgs{
Exec: &aiplatform.GoogleCloudAiplatformV1ProbeExecActionArgs{
Command: pulumi.StringArray{
pulumi.String("string"),
},
},
PeriodSeconds: pulumi.Int(0),
TimeoutSeconds: pulumi.Int(0),
},
},
EncryptionSpec: &aiplatform.GoogleCloudAiplatformV1EncryptionSpecArgs{
KmsKeyName: pulumi.String("string"),
},
Etag: pulumi.String("string"),
ArtifactUri: pulumi.String("string"),
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
Description: pulumi.String("string"),
MetadataSchemaUri: pulumi.String("string"),
Name: pulumi.String("string"),
PipelineJob: pulumi.String("string"),
PredictSchemata: &aiplatform.GoogleCloudAiplatformV1PredictSchemataArgs{
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"),
})

    var trainingPipelineResource = new TrainingPipeline("trainingPipelineResource", TrainingPipelineArgs.builder()
    .displayName("string")
    .trainingTaskDefinition("string")
    .trainingTaskInputs("any")
    .encryptionSpec(GoogleCloudAiplatformV1EncryptionSpecArgs.builder()
        .kmsKeyName("string")
        .build())
    .inputDataConfig(GoogleCloudAiplatformV1InputDataConfigArgs.builder()
        .datasetId("string")
        .annotationSchemaUri("string")
        .annotationsFilter("string")
        .bigqueryDestination(GoogleCloudAiplatformV1BigQueryDestinationArgs.builder()
            .outputUri("string")
            .build())
        .filterSplit(GoogleCloudAiplatformV1FilterSplitArgs.builder()
            .testFilter("string")
            .trainingFilter("string")
            .validationFilter("string")
            .build())
        .fractionSplit(GoogleCloudAiplatformV1FractionSplitArgs.builder()
            .testFraction(0)
            .trainingFraction(0)
            .validationFraction(0)
            .build())
        .gcsDestination(GoogleCloudAiplatformV1GcsDestinationArgs.builder()
            .outputUriPrefix("string")
            .build())
        .persistMlUseAssignment(false)
        .predefinedSplit(GoogleCloudAiplatformV1PredefinedSplitArgs.builder()
            .key("string")
            .build())
        .savedQueryId("string")
        .stratifiedSplit(GoogleCloudAiplatformV1StratifiedSplitArgs.builder()
            .key("string")
            .testFraction(0)
            .trainingFraction(0)
            .validationFraction(0)
            .build())
        .timestampSplit(GoogleCloudAiplatformV1TimestampSplitArgs.builder()
            .key("string")
            .testFraction(0)
            .trainingFraction(0)
            .validationFraction(0)
            .build())
        .build())
    .labels(Map.of("string", "string"))
    .location("string")
    .modelId("string")
    .modelToUpload(GoogleCloudAiplatformV1ModelArgs.builder()
        .displayName("string")
        .explanationSpec(GoogleCloudAiplatformV1ExplanationSpecArgs.builder()
            .parameters(GoogleCloudAiplatformV1ExplanationParametersArgs.builder()
                .examples(GoogleCloudAiplatformV1ExamplesArgs.builder()
                    .exampleGcsSource(GoogleCloudAiplatformV1ExamplesExampleGcsSourceArgs.builder()
                        .dataFormat("DATA_FORMAT_UNSPECIFIED")
                        .gcsSource(GoogleCloudAiplatformV1GcsSourceArgs.builder()
                            .uris("string")
                            .build())
                        .build())
                    .nearestNeighborSearchConfig("any")
                    .neighborCount(0)
                    .presets(GoogleCloudAiplatformV1PresetsArgs.builder()
                        .modality("MODALITY_UNSPECIFIED")
                        .query("PRECISE")
                        .build())
                    .build())
                .integratedGradientsAttribution(GoogleCloudAiplatformV1IntegratedGradientsAttributionArgs.builder()
                    .stepCount(0)
                    .blurBaselineConfig(GoogleCloudAiplatformV1BlurBaselineConfigArgs.builder()
                        .maxBlurSigma(0)
                        .build())
                    .smoothGradConfig(GoogleCloudAiplatformV1SmoothGradConfigArgs.builder()
                        .featureNoiseSigma(GoogleCloudAiplatformV1FeatureNoiseSigmaArgs.builder()
                            .noiseSigma(GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs.builder()
                                .name("string")
                                .sigma(0)
                                .build())
                            .build())
                        .noiseSigma(0)
                        .noisySampleCount(0)
                        .build())
                    .build())
                .outputIndices("any")
                .sampledShapleyAttribution(GoogleCloudAiplatformV1SampledShapleyAttributionArgs.builder()
                    .pathCount(0)
                    .build())
                .topK(0)
                .xraiAttribution(GoogleCloudAiplatformV1XraiAttributionArgs.builder()
                    .stepCount(0)
                    .blurBaselineConfig(GoogleCloudAiplatformV1BlurBaselineConfigArgs.builder()
                        .maxBlurSigma(0)
                        .build())
                    .smoothGradConfig(GoogleCloudAiplatformV1SmoothGradConfigArgs.builder()
                        .featureNoiseSigma(GoogleCloudAiplatformV1FeatureNoiseSigmaArgs.builder()
                            .noiseSigma(GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs.builder()
                                .name("string")
                                .sigma(0)
                                .build())
                            .build())
                        .noiseSigma(0)
                        .noisySampleCount(0)
                        .build())
                    .build())
                .build())
            .metadata(GoogleCloudAiplatformV1ExplanationMetadataArgs.builder()
                .inputs(Map.of("string", "string"))
                .outputs(Map.of("string", "string"))
                .featureAttributionsSchemaUri("string")
                .latentSpaceSource("string")
                .build())
            .build())
        .metadata("any")
        .containerSpec(GoogleCloudAiplatformV1ModelContainerSpecArgs.builder()
            .imageUri("string")
            .args("string")
            .command("string")
            .deploymentTimeout("string")
            .env(GoogleCloudAiplatformV1EnvVarArgs.builder()
                .name("string")
                .value("string")
                .build())
            .healthProbe(GoogleCloudAiplatformV1ProbeArgs.builder()
                .exec(GoogleCloudAiplatformV1ProbeExecActionArgs.builder()
                    .command("string")
                    .build())
                .periodSeconds(0)
                .timeoutSeconds(0)
                .build())
            .healthRoute("string")
            .ports(GoogleCloudAiplatformV1PortArgs.builder()
                .containerPort(0)
                .build())
            .predictRoute("string")
            .sharedMemorySizeMb("string")
            .startupProbe(GoogleCloudAiplatformV1ProbeArgs.builder()
                .exec(GoogleCloudAiplatformV1ProbeExecActionArgs.builder()
                    .command("string")
                    .build())
                .periodSeconds(0)
                .timeoutSeconds(0)
                .build())
            .build())
        .encryptionSpec(GoogleCloudAiplatformV1EncryptionSpecArgs.builder()
            .kmsKeyName("string")
            .build())
        .etag("string")
        .artifactUri("string")
        .labels(Map.of("string", "string"))
        .description("string")
        .metadataSchemaUri("string")
        .name("string")
        .pipelineJob("string")
        .predictSchemata(GoogleCloudAiplatformV1PredictSchemataArgs.builder()
            .instanceSchemaUri("string")
            .parametersSchemaUri("string")
            .predictionSchemaUri("string")
            .build())
        .versionAliases("string")
        .versionDescription("string")
        .build())
    .parentModel("string")
    .project("string")
    .build());

    training_pipeline_resource = google_native.aiplatform.v1.TrainingPipeline("trainingPipelineResource",
    display_name="string",
    training_task_definition="string",
    training_task_inputs="any",
    encryption_spec=google_native.aiplatform.v1.GoogleCloudAiplatformV1EncryptionSpecArgs(
        kms_key_name="string",
    ),
    input_data_config=google_native.aiplatform.v1.GoogleCloudAiplatformV1InputDataConfigArgs(
        dataset_id="string",
        annotation_schema_uri="string",
        annotations_filter="string",
        bigquery_destination=google_native.aiplatform.v1.GoogleCloudAiplatformV1BigQueryDestinationArgs(
            output_uri="string",
        ),
        filter_split=google_native.aiplatform.v1.GoogleCloudAiplatformV1FilterSplitArgs(
            test_filter="string",
            training_filter="string",
            validation_filter="string",
        ),
        fraction_split=google_native.aiplatform.v1.GoogleCloudAiplatformV1FractionSplitArgs(
            test_fraction=0,
            training_fraction=0,
            validation_fraction=0,
        ),
        gcs_destination=google_native.aiplatform.v1.GoogleCloudAiplatformV1GcsDestinationArgs(
            output_uri_prefix="string",
        ),
        persist_ml_use_assignment=False,
        predefined_split=google_native.aiplatform.v1.GoogleCloudAiplatformV1PredefinedSplitArgs(
            key="string",
        ),
        saved_query_id="string",
        stratified_split=google_native.aiplatform.v1.GoogleCloudAiplatformV1StratifiedSplitArgs(
            key="string",
            test_fraction=0,
            training_fraction=0,
            validation_fraction=0,
        ),
        timestamp_split=google_native.aiplatform.v1.GoogleCloudAiplatformV1TimestampSplitArgs(
            key="string",
            test_fraction=0,
            training_fraction=0,
            validation_fraction=0,
        ),
    ),
    labels={
        "string": "string",
    },
    location="string",
    model_id="string",
    model_to_upload=google_native.aiplatform.v1.GoogleCloudAiplatformV1ModelArgs(
        display_name="string",
        explanation_spec=google_native.aiplatform.v1.GoogleCloudAiplatformV1ExplanationSpecArgs(
            parameters=google_native.aiplatform.v1.GoogleCloudAiplatformV1ExplanationParametersArgs(
                examples=google_native.aiplatform.v1.GoogleCloudAiplatformV1ExamplesArgs(
                    example_gcs_source=google_native.aiplatform.v1.GoogleCloudAiplatformV1ExamplesExampleGcsSourceArgs(
                        data_format=google_native.aiplatform.v1.GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat.DATA_FORMAT_UNSPECIFIED,
                        gcs_source=google_native.aiplatform.v1.GoogleCloudAiplatformV1GcsSourceArgs(
                            uris=["string"],
                        ),
                    ),
                    nearest_neighbor_search_config="any",
                    neighbor_count=0,
                    presets=google_native.aiplatform.v1.GoogleCloudAiplatformV1PresetsArgs(
                        modality=google_native.aiplatform.v1.GoogleCloudAiplatformV1PresetsModality.MODALITY_UNSPECIFIED,
                        query=google_native.aiplatform.v1.GoogleCloudAiplatformV1PresetsQuery.PRECISE,
                    ),
                ),
                integrated_gradients_attribution=google_native.aiplatform.v1.GoogleCloudAiplatformV1IntegratedGradientsAttributionArgs(
                    step_count=0,
                    blur_baseline_config=google_native.aiplatform.v1.GoogleCloudAiplatformV1BlurBaselineConfigArgs(
                        max_blur_sigma=0,
                    ),
                    smooth_grad_config=google_native.aiplatform.v1.GoogleCloudAiplatformV1SmoothGradConfigArgs(
                        feature_noise_sigma=google_native.aiplatform.v1.GoogleCloudAiplatformV1FeatureNoiseSigmaArgs(
                            noise_sigma=[google_native.aiplatform.v1.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs(
                                name="string",
                                sigma=0,
                            )],
                        ),
                        noise_sigma=0,
                        noisy_sample_count=0,
                    ),
                ),
                output_indices=["any"],
                sampled_shapley_attribution=google_native.aiplatform.v1.GoogleCloudAiplatformV1SampledShapleyAttributionArgs(
                    path_count=0,
                ),
                top_k=0,
                xrai_attribution=google_native.aiplatform.v1.GoogleCloudAiplatformV1XraiAttributionArgs(
                    step_count=0,
                    blur_baseline_config=google_native.aiplatform.v1.GoogleCloudAiplatformV1BlurBaselineConfigArgs(
                        max_blur_sigma=0,
                    ),
                    smooth_grad_config=google_native.aiplatform.v1.GoogleCloudAiplatformV1SmoothGradConfigArgs(
                        feature_noise_sigma=google_native.aiplatform.v1.GoogleCloudAiplatformV1FeatureNoiseSigmaArgs(
                            noise_sigma=[google_native.aiplatform.v1.GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs(
                                name="string",
                                sigma=0,
                            )],
                        ),
                        noise_sigma=0,
                        noisy_sample_count=0,
                    ),
                ),
            ),
            metadata=google_native.aiplatform.v1.GoogleCloudAiplatformV1ExplanationMetadataArgs(
                inputs={
                    "string": "string",
                },
                outputs={
                    "string": "string",
                },
                feature_attributions_schema_uri="string",
                latent_space_source="string",
            ),
        ),
        metadata="any",
        container_spec=google_native.aiplatform.v1.GoogleCloudAiplatformV1ModelContainerSpecArgs(
            image_uri="string",
            args=["string"],
            command=["string"],
            deployment_timeout="string",
            env=[google_native.aiplatform.v1.GoogleCloudAiplatformV1EnvVarArgs(
                name="string",
                value="string",
            )],
            health_probe=google_native.aiplatform.v1.GoogleCloudAiplatformV1ProbeArgs(
                exec_=google_native.aiplatform.v1.GoogleCloudAiplatformV1ProbeExecActionArgs(
                    command=["string"],
                ),
                period_seconds=0,
                timeout_seconds=0,
            ),
            health_route="string",
            ports=[google_native.aiplatform.v1.GoogleCloudAiplatformV1PortArgs(
                container_port=0,
            )],
            predict_route="string",
            shared_memory_size_mb="string",
            startup_probe=google_native.aiplatform.v1.GoogleCloudAiplatformV1ProbeArgs(
                exec_=google_native.aiplatform.v1.GoogleCloudAiplatformV1ProbeExecActionArgs(
                    command=["string"],
                ),
                period_seconds=0,
                timeout_seconds=0,
            ),
        ),
        encryption_spec=google_native.aiplatform.v1.GoogleCloudAiplatformV1EncryptionSpecArgs(
            kms_key_name="string",
        ),
        etag="string",
        artifact_uri="string",
        labels={
            "string": "string",
        },
        description="string",
        metadata_schema_uri="string",
        name="string",
        pipeline_job="string",
        predict_schemata=google_native.aiplatform.v1.GoogleCloudAiplatformV1PredictSchemataArgs(
            instance_schema_uri="string",
            parameters_schema_uri="string",
            prediction_schema_uri="string",
        ),
        version_aliases=["string"],
        version_description="string",
    ),
    parent_model="string",
    project="string")

    const trainingPipelineResource = new google_native.aiplatform.v1.TrainingPipeline("trainingPipelineResource", {
    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.v1.GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat.DataFormatUnspecified,
                        gcsSource: {
                            uris: ["string"],
                        },
                    },
                    nearestNeighborSearchConfig: "any",
                    neighborCount: 0,
                    presets: {
                        modality: google_native.aiplatform.v1.GoogleCloudAiplatformV1PresetsModality.ModalityUnspecified,
                        query: google_native.aiplatform.v1.GoogleCloudAiplatformV1PresetsQuery.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",
            },
        },
        metadata: "any",
        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",
        },
        description: "string",
        metadataSchemaUri: "string",
        name: "string",
        pipelineJob: "string",
        predictSchemata: {
            instanceSchemaUri: "string",
            parametersSchemaUri: "string",
            predictionSchemaUri: "string",
        },
        versionAliases: ["string"],
        versionDescription: "string",
    },
    parentModel: "string",
    project: "string",
});

    type: google-native:aiplatform/v1: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
                    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
        pipelineJob: string
        predictSchemata:
            instanceSchemaUri: string
            parametersSchemaUri: string
            predictionSchemaUri: string
        versionAliases:
            - string
        versionDescription: string
    parentModel: string
    project: string
    trainingTaskDefinition: string
    trainingTaskInputs: any

    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

    The TrainingPipeline resource accepts the following input properties:

    DisplayName string
    The user-defined name of this TrainingPipeline.
    TrainingTaskDefinition 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 object
    The training task's parameter(s), as specified in the training_task_definition's inputs.
    EncryptionSpec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EncryptionSpec
    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.V1.Inputs.GoogleCloudAiplatformV1InputDataConfig
    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 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.V1.Inputs.GoogleCloudAiplatformV1Model
    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 string
    DisplayName string
    The user-defined name of this TrainingPipeline.
    TrainingTaskDefinition 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 interface{}
    The training task's parameter(s), as specified in the training_task_definition's inputs.
    EncryptionSpec GoogleCloudAiplatformV1EncryptionSpecArgs
    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 GoogleCloudAiplatformV1InputDataConfigArgs
    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 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 GoogleCloudAiplatformV1ModelArgs
    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 string
    displayName String
    The user-defined name of this TrainingPipeline.
    trainingTaskDefinition 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 Object
    The training task's parameter(s), as specified in the training_task_definition's inputs.
    encryptionSpec GoogleCloudAiplatformV1EncryptionSpec
    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 GoogleCloudAiplatformV1InputDataConfig
    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 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 GoogleCloudAiplatformV1Model
    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 String
    displayName string
    The user-defined name of this TrainingPipeline.
    trainingTaskDefinition 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 any
    The training task's parameter(s), as specified in the training_task_definition's inputs.
    encryptionSpec GoogleCloudAiplatformV1EncryptionSpec
    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 GoogleCloudAiplatformV1InputDataConfig
    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 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 GoogleCloudAiplatformV1Model
    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 string
    display_name str
    The user-defined name of this TrainingPipeline.
    training_task_definition 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 Any
    The training task's parameter(s), as specified in the training_task_definition's inputs.
    encryption_spec GoogleCloudAiplatformV1EncryptionSpecArgs
    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 GoogleCloudAiplatformV1InputDataConfigArgs
    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 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 GoogleCloudAiplatformV1ModelArgs
    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 str
    displayName String
    The user-defined name of this TrainingPipeline.
    trainingTaskDefinition 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 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 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 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.V1.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

    GoogleCloudAiplatformV1BigQueryDestination, GoogleCloudAiplatformV1BigQueryDestinationArgs

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

    GoogleCloudAiplatformV1BigQueryDestinationResponse, GoogleCloudAiplatformV1BigQueryDestinationResponseArgs

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

    GoogleCloudAiplatformV1BlurBaselineConfig, GoogleCloudAiplatformV1BlurBaselineConfigArgs

    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.

    GoogleCloudAiplatformV1BlurBaselineConfigResponse, GoogleCloudAiplatformV1BlurBaselineConfigResponseArgs

    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.

    GoogleCloudAiplatformV1DeployedModelRefResponse, GoogleCloudAiplatformV1DeployedModelRefResponseArgs

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

    GoogleCloudAiplatformV1EncryptionSpec, GoogleCloudAiplatformV1EncryptionSpecArgs

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

    GoogleCloudAiplatformV1EncryptionSpecResponse, GoogleCloudAiplatformV1EncryptionSpecResponseArgs

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

    GoogleCloudAiplatformV1EnvVar, GoogleCloudAiplatformV1EnvVarArgs

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

    GoogleCloudAiplatformV1EnvVarResponse, GoogleCloudAiplatformV1EnvVarResponseArgs

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

    GoogleCloudAiplatformV1Examples, GoogleCloudAiplatformV1ExamplesArgs

    ExampleGcsSource Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExamplesExampleGcsSource
    The Cloud Storage input instances.
    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.V1.Inputs.GoogleCloudAiplatformV1Presets
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    ExampleGcsSource GoogleCloudAiplatformV1ExamplesExampleGcsSource
    The Cloud Storage input instances.
    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 GoogleCloudAiplatformV1Presets
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    exampleGcsSource GoogleCloudAiplatformV1ExamplesExampleGcsSource
    The Cloud Storage input instances.
    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 GoogleCloudAiplatformV1Presets
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    exampleGcsSource GoogleCloudAiplatformV1ExamplesExampleGcsSource
    The Cloud Storage input instances.
    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 GoogleCloudAiplatformV1Presets
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    example_gcs_source GoogleCloudAiplatformV1ExamplesExampleGcsSource
    The Cloud Storage input instances.
    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 GoogleCloudAiplatformV1Presets
    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.
    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.

    GoogleCloudAiplatformV1ExamplesExampleGcsSource, GoogleCloudAiplatformV1ExamplesExampleGcsSourceArgs

    DataFormat Pulumi.GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat
    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.V1.Inputs.GoogleCloudAiplatformV1GcsSource
    The Cloud Storage location for the input instances.
    DataFormat GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    GcsSource GoogleCloudAiplatformV1GcsSource
    The Cloud Storage location for the input instances.
    dataFormat GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcsSource GoogleCloudAiplatformV1GcsSource
    The Cloud Storage location for the input instances.
    dataFormat GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcsSource GoogleCloudAiplatformV1GcsSource
    The Cloud Storage location for the input instances.
    data_format GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcs_source GoogleCloudAiplatformV1GcsSource
    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.

    GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormat, GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormatArgs

    DataFormatUnspecified
    DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
    Jsonl
    JSONLExamples are stored in JSONL files.
    GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormatDataFormatUnspecified
    DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
    GoogleCloudAiplatformV1ExamplesExampleGcsSourceDataFormatJsonl
    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.

    GoogleCloudAiplatformV1ExamplesExampleGcsSourceResponse, GoogleCloudAiplatformV1ExamplesExampleGcsSourceResponseArgs

    DataFormat string
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    GcsSource Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsSourceResponse
    The Cloud Storage location for the input instances.
    DataFormat string
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    GcsSource GoogleCloudAiplatformV1GcsSourceResponse
    The Cloud Storage location for the input instances.
    dataFormat String
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcsSource GoogleCloudAiplatformV1GcsSourceResponse
    The Cloud Storage location for the input instances.
    dataFormat string
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcsSource GoogleCloudAiplatformV1GcsSourceResponse
    The Cloud Storage location for the input instances.
    data_format str
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcs_source GoogleCloudAiplatformV1GcsSourceResponse
    The Cloud Storage location for the input instances.
    dataFormat String
    The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
    gcsSource Property Map
    The Cloud Storage location for the input instances.

    GoogleCloudAiplatformV1ExamplesResponse, GoogleCloudAiplatformV1ExamplesResponseArgs

    ExampleGcsSource Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExamplesExampleGcsSourceResponse
    The Cloud Storage input instances.
    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.V1.Inputs.GoogleCloudAiplatformV1PresetsResponse
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    ExampleGcsSource GoogleCloudAiplatformV1ExamplesExampleGcsSourceResponse
    The Cloud Storage input instances.
    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 GoogleCloudAiplatformV1PresetsResponse
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    exampleGcsSource GoogleCloudAiplatformV1ExamplesExampleGcsSourceResponse
    The Cloud Storage input instances.
    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 GoogleCloudAiplatformV1PresetsResponse
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    exampleGcsSource GoogleCloudAiplatformV1ExamplesExampleGcsSourceResponse
    The Cloud Storage input instances.
    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 GoogleCloudAiplatformV1PresetsResponse
    Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
    example_gcs_source GoogleCloudAiplatformV1ExamplesExampleGcsSourceResponse
    The Cloud Storage input instances.
    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 GoogleCloudAiplatformV1PresetsResponse
    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.
    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.

    GoogleCloudAiplatformV1ExplanationMetadata, GoogleCloudAiplatformV1ExplanationMetadataArgs

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

    GoogleCloudAiplatformV1ExplanationMetadataResponse, GoogleCloudAiplatformV1ExplanationMetadataResponseArgs

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

    GoogleCloudAiplatformV1ExplanationParameters, GoogleCloudAiplatformV1ExplanationParametersArgs

    Examples Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1Examples
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    IntegratedGradientsAttribution Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1IntegratedGradientsAttribution
    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.V1.Inputs.GoogleCloudAiplatformV1SampledShapleyAttribution
    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.V1.Inputs.GoogleCloudAiplatformV1XraiAttribution
    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 GoogleCloudAiplatformV1Examples
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    IntegratedGradientsAttribution GoogleCloudAiplatformV1IntegratedGradientsAttribution
    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 GoogleCloudAiplatformV1SampledShapleyAttribution
    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 GoogleCloudAiplatformV1XraiAttribution
    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 GoogleCloudAiplatformV1Examples
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    integratedGradientsAttribution GoogleCloudAiplatformV1IntegratedGradientsAttribution
    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 GoogleCloudAiplatformV1SampledShapleyAttribution
    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 GoogleCloudAiplatformV1XraiAttribution
    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 GoogleCloudAiplatformV1Examples
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    integratedGradientsAttribution GoogleCloudAiplatformV1IntegratedGradientsAttribution
    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 GoogleCloudAiplatformV1SampledShapleyAttribution
    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 GoogleCloudAiplatformV1XraiAttribution
    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 GoogleCloudAiplatformV1Examples
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    integrated_gradients_attribution GoogleCloudAiplatformV1IntegratedGradientsAttribution
    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 GoogleCloudAiplatformV1SampledShapleyAttribution
    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 GoogleCloudAiplatformV1XraiAttribution
    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.

    GoogleCloudAiplatformV1ExplanationParametersResponse, GoogleCloudAiplatformV1ExplanationParametersResponseArgs

    Examples Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExamplesResponse
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    IntegratedGradientsAttribution Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1IntegratedGradientsAttributionResponse
    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.V1.Inputs.GoogleCloudAiplatformV1SampledShapleyAttributionResponse
    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.V1.Inputs.GoogleCloudAiplatformV1XraiAttributionResponse
    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 GoogleCloudAiplatformV1ExamplesResponse
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    IntegratedGradientsAttribution GoogleCloudAiplatformV1IntegratedGradientsAttributionResponse
    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 GoogleCloudAiplatformV1SampledShapleyAttributionResponse
    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 GoogleCloudAiplatformV1XraiAttributionResponse
    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 GoogleCloudAiplatformV1ExamplesResponse
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    integratedGradientsAttribution GoogleCloudAiplatformV1IntegratedGradientsAttributionResponse
    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 GoogleCloudAiplatformV1SampledShapleyAttributionResponse
    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 GoogleCloudAiplatformV1XraiAttributionResponse
    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 GoogleCloudAiplatformV1ExamplesResponse
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    integratedGradientsAttribution GoogleCloudAiplatformV1IntegratedGradientsAttributionResponse
    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 GoogleCloudAiplatformV1SampledShapleyAttributionResponse
    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 GoogleCloudAiplatformV1XraiAttributionResponse
    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 GoogleCloudAiplatformV1ExamplesResponse
    Example-based explanations that returns the nearest neighbors from the provided dataset.
    integrated_gradients_attribution GoogleCloudAiplatformV1IntegratedGradientsAttributionResponse
    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 GoogleCloudAiplatformV1SampledShapleyAttributionResponse
    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 GoogleCloudAiplatformV1XraiAttributionResponse
    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.

    GoogleCloudAiplatformV1ExplanationSpec, GoogleCloudAiplatformV1ExplanationSpecArgs

    Parameters Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationParameters
    Parameters that configure explaining of the Model's predictions.
    Metadata Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationMetadata
    Optional. Metadata describing the Model's input and output for explanation.
    Parameters GoogleCloudAiplatformV1ExplanationParameters
    Parameters that configure explaining of the Model's predictions.
    Metadata GoogleCloudAiplatformV1ExplanationMetadata
    Optional. Metadata describing the Model's input and output for explanation.
    parameters GoogleCloudAiplatformV1ExplanationParameters
    Parameters that configure explaining of the Model's predictions.
    metadata GoogleCloudAiplatformV1ExplanationMetadata
    Optional. Metadata describing the Model's input and output for explanation.
    parameters GoogleCloudAiplatformV1ExplanationParameters
    Parameters that configure explaining of the Model's predictions.
    metadata GoogleCloudAiplatformV1ExplanationMetadata
    Optional. Metadata describing the Model's input and output for explanation.
    parameters GoogleCloudAiplatformV1ExplanationParameters
    Parameters that configure explaining of the Model's predictions.
    metadata GoogleCloudAiplatformV1ExplanationMetadata
    Optional. Metadata describing the Model's input and output for explanation.
    parameters Property Map
    Parameters that configure explaining of the Model's predictions.
    metadata Property Map
    Optional. Metadata describing the Model's input and output for explanation.

    GoogleCloudAiplatformV1ExplanationSpecResponse, GoogleCloudAiplatformV1ExplanationSpecResponseArgs

    Metadata Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationMetadataResponse
    Optional. Metadata describing the Model's input and output for explanation.
    Parameters Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ExplanationParametersResponse
    Parameters that configure explaining of the Model's predictions.
    Metadata GoogleCloudAiplatformV1ExplanationMetadataResponse
    Optional. Metadata describing the Model's input and output for explanation.
    Parameters GoogleCloudAiplatformV1ExplanationParametersResponse
    Parameters that configure explaining of the Model's predictions.
    metadata GoogleCloudAiplatformV1ExplanationMetadataResponse
    Optional. Metadata describing the Model's input and output for explanation.
    parameters GoogleCloudAiplatformV1ExplanationParametersResponse
    Parameters that configure explaining of the Model's predictions.
    metadata GoogleCloudAiplatformV1ExplanationMetadataResponse
    Optional. Metadata describing the Model's input and output for explanation.
    parameters GoogleCloudAiplatformV1ExplanationParametersResponse
    Parameters that configure explaining of the Model's predictions.
    metadata GoogleCloudAiplatformV1ExplanationMetadataResponse
    Optional. Metadata describing the Model's input and output for explanation.
    parameters GoogleCloudAiplatformV1ExplanationParametersResponse
    Parameters that configure explaining of the Model's predictions.
    metadata Property Map
    Optional. Metadata describing the Model's input and output for explanation.
    parameters Property Map
    Parameters that configure explaining of the Model's predictions.

    GoogleCloudAiplatformV1FeatureNoiseSigma, GoogleCloudAiplatformV1FeatureNoiseSigmaArgs

    NoiseSigma []GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeature
    Noise sigma per feature. No noise is added to features that are not set.
    noiseSigma List<GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeature>
    Noise sigma per feature. No noise is added to features that are not set.
    noiseSigma GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeature[]
    Noise sigma per feature. No noise is added to features that are not set.
    noise_sigma Sequence[GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeature]
    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.

    GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeature, GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureArgs

    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.

    GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureResponse, GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureResponseArgs

    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.

    GoogleCloudAiplatformV1FeatureNoiseSigmaResponse, GoogleCloudAiplatformV1FeatureNoiseSigmaResponseArgs

    NoiseSigma []GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureResponse
    Noise sigma per feature. No noise is added to features that are not set.
    noiseSigma List<GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureResponse>
    Noise sigma per feature. No noise is added to features that are not set.
    noiseSigma GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureResponse[]
    Noise sigma per feature. No noise is added to features that are not set.
    noise_sigma Sequence[GoogleCloudAiplatformV1FeatureNoiseSigmaNoiseSigmaForFeatureResponse]
    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.

    GoogleCloudAiplatformV1FilterSplit, GoogleCloudAiplatformV1FilterSplitArgs

    TestFilter 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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.

    GoogleCloudAiplatformV1FilterSplitResponse, GoogleCloudAiplatformV1FilterSplitResponseArgs

    TestFilter 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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.

    GoogleCloudAiplatformV1FractionSplit, GoogleCloudAiplatformV1FractionSplitArgs

    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.

    GoogleCloudAiplatformV1FractionSplitResponse, GoogleCloudAiplatformV1FractionSplitResponseArgs

    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.

    GoogleCloudAiplatformV1GcsDestination, GoogleCloudAiplatformV1GcsDestinationArgs

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

    GoogleCloudAiplatformV1GcsDestinationResponse, GoogleCloudAiplatformV1GcsDestinationResponseArgs

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

    GoogleCloudAiplatformV1GcsSource, GoogleCloudAiplatformV1GcsSourceArgs

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

    GoogleCloudAiplatformV1GcsSourceResponse, GoogleCloudAiplatformV1GcsSourceResponseArgs

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

    GoogleCloudAiplatformV1InputDataConfig, GoogleCloudAiplatformV1InputDataConfigArgs

    DatasetId 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.V1.Inputs.GoogleCloudAiplatformV1BigQueryDestination
    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.V1.Inputs.GoogleCloudAiplatformV1FilterSplit
    Split based on the provided filters for each set.
    FractionSplit Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FractionSplit
    Split based on fractions defining the size of each set.
    GcsDestination Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsDestination
    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.V1.Inputs.GoogleCloudAiplatformV1PredefinedSplit
    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.V1.Inputs.GoogleCloudAiplatformV1StratifiedSplit
    Supported only for tabular Datasets. Split based on the distribution of the specified column.
    TimestampSplit Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1TimestampSplit
    Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
    DatasetId 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 GoogleCloudAiplatformV1BigQueryDestination
    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 GoogleCloudAiplatformV1FilterSplit
    Split based on the provided filters for each set.
    FractionSplit GoogleCloudAiplatformV1FractionSplit
    Split based on fractions defining the size of each set.
    GcsDestination GoogleCloudAiplatformV1GcsDestination
    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 GoogleCloudAiplatformV1PredefinedSplit
    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 GoogleCloudAiplatformV1StratifiedSplit
    Supported only for tabular Datasets. Split based on the distribution of the specified column.
    TimestampSplit GoogleCloudAiplatformV1TimestampSplit
    Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
    datasetId 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 GoogleCloudAiplatformV1BigQueryDestination
    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 GoogleCloudAiplatformV1FilterSplit
    Split based on the provided filters for each set.
    fractionSplit GoogleCloudAiplatformV1FractionSplit
    Split based on fractions defining the size of each set.
    gcsDestination GoogleCloudAiplatformV1GcsDestination
    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 GoogleCloudAiplatformV1PredefinedSplit
    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 GoogleCloudAiplatformV1StratifiedSplit
    Supported only for tabular Datasets. Split based on the distribution of the specified column.
    timestampSplit GoogleCloudAiplatformV1TimestampSplit
    Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
    datasetId 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 GoogleCloudAiplatformV1BigQueryDestination
    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 GoogleCloudAiplatformV1FilterSplit
    Split based on the provided filters for each set.
    fractionSplit GoogleCloudAiplatformV1FractionSplit
    Split based on fractions defining the size of each set.
    gcsDestination GoogleCloudAiplatformV1GcsDestination
    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 GoogleCloudAiplatformV1PredefinedSplit
    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 GoogleCloudAiplatformV1StratifiedSplit
    Supported only for tabular Datasets. Split based on the distribution of the specified column.
    timestampSplit GoogleCloudAiplatformV1TimestampSplit
    Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
    dataset_id 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 GoogleCloudAiplatformV1BigQueryDestination
    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 GoogleCloudAiplatformV1FilterSplit
    Split based on the provided filters for each set.
    fraction_split GoogleCloudAiplatformV1FractionSplit
    Split based on fractions defining the size of each set.
    gcs_destination GoogleCloudAiplatformV1GcsDestination
    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 GoogleCloudAiplatformV1PredefinedSplit
    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 GoogleCloudAiplatformV1StratifiedSplit
    Supported only for tabular Datasets. Split based on the distribution of the specified column.
    timestamp_split GoogleCloudAiplatformV1TimestampSplit
    Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
    datasetId 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.

    GoogleCloudAiplatformV1InputDataConfigResponse, GoogleCloudAiplatformV1InputDataConfigResponseArgs

    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.V1.Inputs.GoogleCloudAiplatformV1BigQueryDestinationResponse
    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 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 Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FilterSplitResponse
    Split based on the provided filters for each set.
    FractionSplit Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FractionSplitResponse
    Split based on fractions defining the size of each set.
    GcsDestination Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsDestinationResponse
    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.V1.Inputs.GoogleCloudAiplatformV1PredefinedSplitResponse
    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.V1.Inputs.GoogleCloudAiplatformV1StratifiedSplitResponse
    Supported only for tabular Datasets. Split based on the distribution of the specified column.
    TimestampSplit Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1TimestampSplitResponse
    Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
    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 GoogleCloudAiplatformV1BigQueryDestinationResponse
    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 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 GoogleCloudAiplatformV1FilterSplitResponse
    Split based on the provided filters for each set.
    FractionSplit GoogleCloudAiplatformV1FractionSplitResponse
    Split based on fractions defining the size of each set.
    GcsDestination GoogleCloudAiplatformV1GcsDestinationResponse
    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 GoogleCloudAiplatformV1PredefinedSplitResponse
    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 GoogleCloudAiplatformV1StratifiedSplitResponse
    Supported only for tabular Datasets. Split based on the distribution of the specified column.
    TimestampSplit GoogleCloudAiplatformV1TimestampSplitResponse
    Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
    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 GoogleCloudAiplatformV1BigQueryDestinationResponse
    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 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 GoogleCloudAiplatformV1FilterSplitResponse
    Split based on the provided filters for each set.
    fractionSplit GoogleCloudAiplatformV1FractionSplitResponse
    Split based on fractions defining the size of each set.
    gcsDestination GoogleCloudAiplatformV1GcsDestinationResponse
    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 GoogleCloudAiplatformV1PredefinedSplitResponse
    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 GoogleCloudAiplatformV1StratifiedSplitResponse
    Supported only for tabular Datasets. Split based on the distribution of the specified column.
    timestampSplit GoogleCloudAiplatformV1TimestampSplitResponse
    Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
    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 GoogleCloudAiplatformV1BigQueryDestinationResponse
    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 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 GoogleCloudAiplatformV1FilterSplitResponse
    Split based on the provided filters for each set.
    fractionSplit GoogleCloudAiplatformV1FractionSplitResponse
    Split based on fractions defining the size of each set.
    gcsDestination GoogleCloudAiplatformV1GcsDestinationResponse
    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 GoogleCloudAiplatformV1PredefinedSplitResponse
    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 GoogleCloudAiplatformV1StratifiedSplitResponse
    Supported only for tabular Datasets. Split based on the distribution of the specified column.
    timestampSplit GoogleCloudAiplatformV1TimestampSplitResponse
    Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
    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 GoogleCloudAiplatformV1BigQueryDestinationResponse
    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 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 GoogleCloudAiplatformV1FilterSplitResponse
    Split based on the provided filters for each set.
    fraction_split GoogleCloudAiplatformV1FractionSplitResponse
    Split based on fractions defining the size of each set.
    gcs_destination GoogleCloudAiplatformV1GcsDestinationResponse
    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 GoogleCloudAiplatformV1PredefinedSplitResponse
    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 GoogleCloudAiplatformV1StratifiedSplitResponse
    Supported only for tabular Datasets. Split based on the distribution of the specified column.
    timestamp_split GoogleCloudAiplatformV1TimestampSplitResponse
    Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
    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"
    datasetId 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 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.

    GoogleCloudAiplatformV1IntegratedGradientsAttribution, GoogleCloudAiplatformV1IntegratedGradientsAttributionArgs

    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    BlurBaselineConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BlurBaselineConfig
    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.V1.Inputs.GoogleCloudAiplatformV1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    BlurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfig
    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 GoogleCloudAiplatformV1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Integer
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfig
    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 GoogleCloudAiplatformV1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfig
    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 GoogleCloudAiplatformV1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    step_count int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blur_baseline_config GoogleCloudAiplatformV1BlurBaselineConfig
    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 GoogleCloudAiplatformV1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig Property Map
    Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig Property Map
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

    GoogleCloudAiplatformV1IntegratedGradientsAttributionResponse, GoogleCloudAiplatformV1IntegratedGradientsAttributionResponseArgs

    BlurBaselineConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BlurBaselineConfigResponse
    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.V1.Inputs.GoogleCloudAiplatformV1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    BlurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfigResponse
    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 GoogleCloudAiplatformV1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfigResponse
    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 GoogleCloudAiplatformV1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Integer
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfigResponse
    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 GoogleCloudAiplatformV1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blur_baseline_config GoogleCloudAiplatformV1BlurBaselineConfigResponse
    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 GoogleCloudAiplatformV1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    step_count int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig Property Map
    Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig Property Map
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.

    GoogleCloudAiplatformV1Model, GoogleCloudAiplatformV1ModelArgs

    DisplayName 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.V1.Inputs.GoogleCloudAiplatformV1ModelContainerSpec
    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.V1.Inputs.GoogleCloudAiplatformV1EncryptionSpec
    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.V1.Inputs.GoogleCloudAiplatformV1ExplanationSpec
    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.
    PipelineJob string
    Optional. This field is populated if the model is produced by a pipeline job.
    PredictSchemata Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PredictSchemata
    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 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 GoogleCloudAiplatformV1ModelContainerSpec
    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 GoogleCloudAiplatformV1EncryptionSpec
    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 GoogleCloudAiplatformV1ExplanationSpec
    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.
    PipelineJob string
    Optional. This field is populated if the model is produced by a pipeline job.
    PredictSchemata GoogleCloudAiplatformV1PredictSchemata
    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 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 GoogleCloudAiplatformV1ModelContainerSpec
    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 GoogleCloudAiplatformV1EncryptionSpec
    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 GoogleCloudAiplatformV1ExplanationSpec
    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.
    pipelineJob String
    Optional. This field is populated if the model is produced by a pipeline job.
    predictSchemata GoogleCloudAiplatformV1PredictSchemata
    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 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 GoogleCloudAiplatformV1ModelContainerSpec
    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 GoogleCloudAiplatformV1EncryptionSpec
    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 GoogleCloudAiplatformV1ExplanationSpec
    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.
    pipelineJob string
    Optional. This field is populated if the model is produced by a pipeline job.
    predictSchemata GoogleCloudAiplatformV1PredictSchemata
    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 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 GoogleCloudAiplatformV1ModelContainerSpec
    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 GoogleCloudAiplatformV1EncryptionSpec
    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 GoogleCloudAiplatformV1ExplanationSpec
    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.
    pipeline_job str
    Optional. This field is populated if the model is produced by a pipeline job.
    predict_schemata GoogleCloudAiplatformV1PredictSchemata
    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 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.
    pipelineJob String
    Optional. This field is populated if the model is produced by a pipeline job.
    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.

    GoogleCloudAiplatformV1ModelContainerSpec, GoogleCloudAiplatformV1ModelContainerSpecArgs

    ImageUri string
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    Args List<string>
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    Command List<string>
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    DeploymentTimeout string
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    Env List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EnvVar>
    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.V1.Inputs.GoogleCloudAiplatformV1Probe
    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.V1.Inputs.GoogleCloudAiplatformV1Port>
    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.V1.Inputs.GoogleCloudAiplatformV1Probe
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    ImageUri string
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    Args []string
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    Command []string
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    DeploymentTimeout string
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    Env []GoogleCloudAiplatformV1EnvVar
    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 GoogleCloudAiplatformV1Probe
    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 []GoogleCloudAiplatformV1Port
    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 GoogleCloudAiplatformV1Probe
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    imageUri String
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    args List<String>
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command List<String>
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deploymentTimeout String
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env List<GoogleCloudAiplatformV1EnvVar>
    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 GoogleCloudAiplatformV1Probe
    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<GoogleCloudAiplatformV1Port>
    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 GoogleCloudAiplatformV1Probe
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    imageUri string
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    args string[]
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command string[]
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deploymentTimeout string
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env GoogleCloudAiplatformV1EnvVar[]
    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 GoogleCloudAiplatformV1Probe
    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 GoogleCloudAiplatformV1Port[]
    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 GoogleCloudAiplatformV1Probe
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    image_uri str
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    args Sequence[str]
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command Sequence[str]
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deployment_timeout str
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env Sequence[GoogleCloudAiplatformV1EnvVar]
    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 GoogleCloudAiplatformV1Probe
    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[GoogleCloudAiplatformV1Port]
    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 GoogleCloudAiplatformV1Probe
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    imageUri String
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    args List<String>
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command List<String>
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deploymentTimeout String
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env List<Property Map>
    Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
    healthProbe Property Map
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    healthRoute String
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    ports List<Property Map>
    Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
    predictRoute String
    Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    sharedMemorySizeMb String
    Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
    startupProbe Property Map
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.

    GoogleCloudAiplatformV1ModelContainerSpecResponse, GoogleCloudAiplatformV1ModelContainerSpecResponseArgs

    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.V1.Inputs.GoogleCloudAiplatformV1EnvVarResponse>
    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.V1.Inputs.GoogleCloudAiplatformV1ProbeResponse
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    HealthRoute string
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    ImageUri string
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    Ports List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PortResponse>
    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.V1.Inputs.GoogleCloudAiplatformV1ProbeResponse
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    Args []string
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    Command []string
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    DeploymentTimeout string
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    Env []GoogleCloudAiplatformV1EnvVarResponse
    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 GoogleCloudAiplatformV1ProbeResponse
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    HealthRoute string
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    ImageUri string
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    Ports []GoogleCloudAiplatformV1PortResponse
    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 GoogleCloudAiplatformV1ProbeResponse
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    args List<String>
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command List<String>
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deploymentTimeout String
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env List<GoogleCloudAiplatformV1EnvVarResponse>
    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 GoogleCloudAiplatformV1ProbeResponse
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    healthRoute String
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    imageUri String
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    ports List<GoogleCloudAiplatformV1PortResponse>
    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 GoogleCloudAiplatformV1ProbeResponse
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    args string[]
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command string[]
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deploymentTimeout string
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env GoogleCloudAiplatformV1EnvVarResponse[]
    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 GoogleCloudAiplatformV1ProbeResponse
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    healthRoute string
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    imageUri string
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    ports GoogleCloudAiplatformV1PortResponse[]
    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 GoogleCloudAiplatformV1ProbeResponse
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    args Sequence[str]
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command Sequence[str]
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deployment_timeout str
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env Sequence[GoogleCloudAiplatformV1EnvVarResponse]
    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 GoogleCloudAiplatformV1ProbeResponse
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    health_route str
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    image_uri str
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    ports Sequence[GoogleCloudAiplatformV1PortResponse]
    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 GoogleCloudAiplatformV1ProbeResponse
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
    args List<String>
    Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's CMD. Specify this field as an array of executable and arguments, similar to a Docker CMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from the command field runs without any additional arguments. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. If you don't specify this field and don't specify the command field, then the container's ENTRYPOINT and CMD determine what runs based on their default behavior. See the Docker documentation about how CMD and ENTRYPOINT interact. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the args field of the Kubernetes Containers v1 core API.
    command List<String>
    Immutable. Specifies the command that runs when the container starts. This overrides the container's ENTRYPOINT. Specify this field as an array of executable and arguments, similar to a Docker ENTRYPOINT's "exec" form, not its "shell" form. If you do not specify this field, then the container's ENTRYPOINT runs, in conjunction with the args field or the container's CMD, if either exists. If this field is not specified and the container does not have an ENTRYPOINT, then refer to the Docker documentation about how CMD and ENTRYPOINT interact. If you specify this field, then you can also specify the args field to provide additional arguments for this command. However, if you specify this field, then the container's CMD is ignored. See the Kubernetes documentation about how the command and args fields interact with a container's ENTRYPOINT and CMD. In this field, you can reference environment variables set by Vertex AI and environment variables set in the env field. You cannot reference environment variables set in the Docker image. In order for environment variables to be expanded, reference them by using the following syntax: $( VARIABLE_NAME) Note that this differs from Bash variable expansion, which does not use parentheses. If a variable cannot be resolved, the reference in the input string is used unchanged. To avoid variable expansion, you can escape this syntax with $$; for example: $$(VARIABLE_NAME) This field corresponds to the command field of the Kubernetes Containers v1 core API.
    deploymentTimeout String
    Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
    env List<Property Map>
    Immutable. List of environment variables to set in the container. After the container starts running, code running in the container can read these environment variables. Additionally, the command and args fields can reference these variables. Later entries in this list can also reference earlier entries. For example, the following example sets the variable VAR_2 to have the value foo bar: json [ { "name": "VAR_1", "value": "foo" }, { "name": "VAR_2", "value": "$(VAR_1) bar" } ] If you switch the order of the variables in the example, then the expansion does not occur. This field corresponds to the env field of the Kubernetes Containers v1 core API.
    healthProbe Property Map
    Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
    healthRoute String
    Immutable. HTTP path on the container to send health checks to. Vertex AI intermittently sends GET requests to this path on the container's IP address and port to check that the container is healthy. Read more about health checks. For example, if you set this field to /bar, then Vertex AI intermittently sends a GET request to the /bar path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/ DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    imageUri String
    Immutable. URI of the Docker image to be used as the custom container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry. Learn more about the container publishing requirements, including permissions requirements for the Vertex AI Service Agent. The container image is ingested upon ModelService.UploadModel, stored internally, and this original path is afterwards not used. To learn about the requirements for the Docker image itself, see Custom container requirements. You can use the URI to one of Vertex AI's pre-built container images for prediction in this field.
    ports List<Property Map>
    Immutable. List of ports to expose from the container. Vertex AI sends any prediction requests that it receives to the first port on this list. Vertex AI also sends liveness and health checks to this port. If you do not specify this field, it defaults to following value: json [ { "containerPort": 8080 } ] Vertex AI does not use ports other than the first one listed. This field corresponds to the ports field of the Kubernetes Containers v1 core API.
    predictRoute String
    Immutable. HTTP path on the container to send prediction requests to. Vertex AI forwards requests sent using projects.locations.endpoints.predict to this path on the container's IP address and port. Vertex AI then returns the container's response in the API response. For example, if you set this field to /foo, then when Vertex AI receives a prediction request, it forwards the request body in a POST request to the /foo path on the port of your container specified by the first value of this ModelContainerSpec's ports field. If you don't specify this field, it defaults to the following value when you deploy this Model to an Endpoint: /v1/endpoints/ENDPOINT/deployedModels/DEPLOYED_MODEL:predict The placeholders in this value are replaced as follows: * ENDPOINT: The last segment (following endpoints/)of the Endpoint.name][] field of the Endpoint where this Model has been deployed. (Vertex AI makes this value available to your container code as the AIP_ENDPOINT_ID environment variable.) * DEPLOYED_MODEL: DeployedModel.id of the DeployedModel. (Vertex AI makes this value available to your container code as the AIP_DEPLOYED_MODEL_ID environment variable.)
    sharedMemorySizeMb String
    Immutable. The amount of the VM memory to reserve as the shared memory for the model in megabytes. TODO (b/306244185): Revise documentation before exposing.
    startupProbe Property Map
    Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.

    GoogleCloudAiplatformV1ModelExportFormatResponse, GoogleCloudAiplatformV1ModelExportFormatResponseArgs

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

    GoogleCloudAiplatformV1ModelOriginalModelInfoResponse, GoogleCloudAiplatformV1ModelOriginalModelInfoResponseArgs

    Model 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 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 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 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 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 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}

    GoogleCloudAiplatformV1ModelResponse, GoogleCloudAiplatformV1ModelResponseArgs

    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.V1.Inputs.GoogleCloudAiplatformV1ModelContainerSpecResponse
    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 string
    Timestamp when this Model was uploaded into Vertex AI.
    DeployedModels List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1DeployedModelRefResponse>
    The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
    Description string
    The description of the Model.
    DisplayName 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 Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EncryptionSpecResponse
    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.V1.Inputs.GoogleCloudAiplatformV1ExplanationSpecResponse
    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.
    MetadataArtifact 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 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 Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ModelSourceInfoResponse
    Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
    Name string
    The resource name of the Model.
    OriginalModelInfo Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ModelOriginalModelInfoResponse
    If this Model is a copy of another Model, this contains info about the original.
    PipelineJob string
    Optional. This field is populated if the model is produced by a pipeline job.
    PredictSchemata Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PredictSchemataResponse
    The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
    SupportedDeploymentResourcesTypes 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 List<Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ModelExportFormatResponse>
    The formats in which this Model may be exported. If empty, this Model is not available for export.
    SupportedInputStorageFormats 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 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 string
    The resource name of the TrainingPipeline that uploaded this Model, if any.
    UpdateTime string
    Timestamp when this Model was most recently updated.
    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.
    VersionCreateTime string
    Timestamp when this version was created.
    VersionDescription string
    The description of this version.
    VersionId 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 string
    Timestamp when this version was most recently updated.
    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 GoogleCloudAiplatformV1ModelContainerSpecResponse
    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 string
    Timestamp when this Model was uploaded into Vertex AI.
    DeployedModels []GoogleCloudAiplatformV1DeployedModelRefResponse
    The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
    Description string
    The description of the Model.
    DisplayName 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 GoogleCloudAiplatformV1EncryptionSpecResponse
    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 GoogleCloudAiplatformV1ExplanationSpecResponse
    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.
    MetadataArtifact 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 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 GoogleCloudAiplatformV1ModelSourceInfoResponse
    Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
    Name string
    The resource name of the Model.
    OriginalModelInfo GoogleCloudAiplatformV1ModelOriginalModelInfoResponse
    If this Model is a copy of another Model, this contains info about the original.
    PipelineJob string
    Optional. This field is populated if the model is produced by a pipeline job.
    PredictSchemata GoogleCloudAiplatformV1PredictSchemataResponse
    The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
    SupportedDeploymentResourcesTypes []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 []GoogleCloudAiplatformV1ModelExportFormatResponse
    The formats in which this Model may be exported. If empty, this Model is not available for export.
    SupportedInputStorageFormats []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 []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 string
    The resource name of the TrainingPipeline that uploaded this Model, if any.
    UpdateTime string
    Timestamp when this Model was most recently updated.
    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.
    VersionCreateTime string
    Timestamp when this version was created.
    VersionDescription string
    The description of this version.
    VersionId 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 string
    Timestamp when this version was most recently updated.
    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 GoogleCloudAiplatformV1ModelContainerSpecResponse
    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 String
    Timestamp when this Model was uploaded into Vertex AI.
    deployedModels List<GoogleCloudAiplatformV1DeployedModelRefResponse>
    The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
    description String
    The description of the Model.
    displayName 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 GoogleCloudAiplatformV1EncryptionSpecResponse
    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 GoogleCloudAiplatformV1ExplanationSpecResponse
    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.
    metadataArtifact 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 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 GoogleCloudAiplatformV1ModelSourceInfoResponse
    Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
    name String
    The resource name of the Model.
    originalModelInfo GoogleCloudAiplatformV1ModelOriginalModelInfoResponse
    If this Model is a copy of another Model, this contains info about the original.
    pipelineJob String
    Optional. This field is populated if the model is produced by a pipeline job.
    predictSchemata GoogleCloudAiplatformV1PredictSchemataResponse
    The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
    supportedDeploymentResourcesTypes 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 List<GoogleCloudAiplatformV1ModelExportFormatResponse>
    The formats in which this Model may be exported. If empty, this Model is not available for export.
    supportedInputStorageFormats 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 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 String
    The resource name of the TrainingPipeline that uploaded this Model, if any.
    updateTime String
    Timestamp when this Model was most recently updated.
    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.
    versionCreateTime String
    Timestamp when this version was created.
    versionDescription String
    The description of this version.
    versionId 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 String
    Timestamp when this version was most recently updated.
    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 GoogleCloudAiplatformV1ModelContainerSpecResponse
    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 string
    Timestamp when this Model was uploaded into Vertex AI.
    deployedModels GoogleCloudAiplatformV1DeployedModelRefResponse[]
    The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
    description string
    The description of the Model.
    displayName 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 GoogleCloudAiplatformV1EncryptionSpecResponse
    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 GoogleCloudAiplatformV1ExplanationSpecResponse
    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.
    metadataArtifact 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 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 GoogleCloudAiplatformV1ModelSourceInfoResponse
    Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
    name string
    The resource name of the Model.
    originalModelInfo GoogleCloudAiplatformV1ModelOriginalModelInfoResponse
    If this Model is a copy of another Model, this contains info about the original.
    pipelineJob string
    Optional. This field is populated if the model is produced by a pipeline job.
    predictSchemata GoogleCloudAiplatformV1PredictSchemataResponse
    The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
    supportedDeploymentResourcesTypes 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 GoogleCloudAiplatformV1ModelExportFormatResponse[]
    The formats in which this Model may be exported. If empty, this Model is not available for export.
    supportedInputStorageFormats 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 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 string
    The resource name of the TrainingPipeline that uploaded this Model, if any.
    updateTime string
    Timestamp when this Model was most recently updated.
    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.
    versionCreateTime string
    Timestamp when this version was created.
    versionDescription string
    The description of this version.
    versionId 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 string
    Timestamp when this version was most recently updated.
    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 GoogleCloudAiplatformV1ModelContainerSpecResponse
    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 str
    Timestamp when this Model was uploaded into Vertex AI.
    deployed_models Sequence[GoogleCloudAiplatformV1DeployedModelRefResponse]
    The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
    description str
    The description of the Model.
    display_name 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 GoogleCloudAiplatformV1EncryptionSpecResponse
    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 GoogleCloudAiplatformV1ExplanationSpecResponse
    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_artifact 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 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 GoogleCloudAiplatformV1ModelSourceInfoResponse
    Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
    name str
    The resource name of the Model.
    original_model_info GoogleCloudAiplatformV1ModelOriginalModelInfoResponse
    If this Model is a copy of another Model, this contains info about the original.
    pipeline_job str
    Optional. This field is populated if the model is produced by a pipeline job.
    predict_schemata GoogleCloudAiplatformV1PredictSchemataResponse
    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 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 Sequence[GoogleCloudAiplatformV1ModelExportFormatResponse]
    The formats in which this Model may be exported. If empty, this Model is not available for export.
    supported_input_storage_formats 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 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 str
    The resource name of the TrainingPipeline that uploaded this Model, if any.
    update_time str
    Timestamp when this Model was most recently updated.
    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_create_time str
    Timestamp when this version was created.
    version_description str
    The description of this version.
    version_id 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 str
    Timestamp when this version was most recently updated.
    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.
    createTime String
    Timestamp when this Model was uploaded into Vertex AI.
    deployedModels List<Property Map>
    The pointers to DeployedModels created from this Model. Note that Model could have been deployed to Endpoints in different Locations.
    description String
    The description of the Model.
    displayName 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 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.
    metadataArtifact 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 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 Property Map
    Source of a model. It can either be automl training pipeline, custom training pipeline, BigQuery ML, or existing Vertex AI Model.
    name String
    The resource name of the Model.
    originalModelInfo Property Map
    If this Model is a copy of another Model, this contains info about the original.
    pipelineJob String
    Optional. This field is populated if the model is produced by a pipeline job.
    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.
    supportedDeploymentResourcesTypes 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 List<Property Map>
    The formats in which this Model may be exported. If empty, this Model is not available for export.
    supportedInputStorageFormats 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 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 String
    The resource name of the TrainingPipeline that uploaded this Model, if any.
    updateTime String
    Timestamp when this Model was most recently updated.
    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.
    versionCreateTime String
    Timestamp when this version was created.
    versionDescription String
    The description of this version.
    versionId 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 String
    Timestamp when this version was most recently updated.

    GoogleCloudAiplatformV1ModelSourceInfoResponse, GoogleCloudAiplatformV1ModelSourceInfoResponseArgs

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

    GoogleCloudAiplatformV1Port, GoogleCloudAiplatformV1PortArgs

    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.

    GoogleCloudAiplatformV1PortResponse, GoogleCloudAiplatformV1PortResponseArgs

    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.

    GoogleCloudAiplatformV1PredefinedSplit, GoogleCloudAiplatformV1PredefinedSplitArgs

    Key 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 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 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 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 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 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.

    GoogleCloudAiplatformV1PredefinedSplitResponse, GoogleCloudAiplatformV1PredefinedSplitResponseArgs

    Key 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 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 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 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 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 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.

    GoogleCloudAiplatformV1PredictSchemata, GoogleCloudAiplatformV1PredictSchemataArgs

    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.

    GoogleCloudAiplatformV1PredictSchemataResponse, GoogleCloudAiplatformV1PredictSchemataResponseArgs

    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.

    GoogleCloudAiplatformV1Presets, GoogleCloudAiplatformV1PresetsArgs

    Modality Pulumi.GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1PresetsModality
    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.V1.GoogleCloudAiplatformV1PresetsQuery
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
    Modality GoogleCloudAiplatformV1PresetsModality
    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 GoogleCloudAiplatformV1PresetsQuery
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
    modality GoogleCloudAiplatformV1PresetsModality
    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 GoogleCloudAiplatformV1PresetsQuery
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
    modality GoogleCloudAiplatformV1PresetsModality
    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 GoogleCloudAiplatformV1PresetsQuery
    Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to PRECISE.
    modality GoogleCloudAiplatformV1PresetsModality
    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 GoogleCloudAiplatformV1PresetsQuery
    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.

    GoogleCloudAiplatformV1PresetsModality, GoogleCloudAiplatformV1PresetsModalityArgs

    ModalityUnspecified
    MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
    Image
    IMAGEIMAGE modality
    Text
    TEXTTEXT modality
    Tabular
    TABULARTABULAR modality
    GoogleCloudAiplatformV1PresetsModalityModalityUnspecified
    MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
    GoogleCloudAiplatformV1PresetsModalityImage
    IMAGEIMAGE modality
    GoogleCloudAiplatformV1PresetsModalityText
    TEXTTEXT modality
    GoogleCloudAiplatformV1PresetsModalityTabular
    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

    GoogleCloudAiplatformV1PresetsQuery, GoogleCloudAiplatformV1PresetsQueryArgs

    Precise
    PRECISEMore precise neighbors as a trade-off against slower response.
    Fast
    FASTFaster response as a trade-off against less precise neighbors.
    GoogleCloudAiplatformV1PresetsQueryPrecise
    PRECISEMore precise neighbors as a trade-off against slower response.
    GoogleCloudAiplatformV1PresetsQueryFast
    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.

    GoogleCloudAiplatformV1PresetsResponse, GoogleCloudAiplatformV1PresetsResponseArgs

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

    GoogleCloudAiplatformV1Probe, GoogleCloudAiplatformV1ProbeArgs

    Exec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ProbeExecAction
    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 GoogleCloudAiplatformV1ProbeExecAction
    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 GoogleCloudAiplatformV1ProbeExecAction
    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 GoogleCloudAiplatformV1ProbeExecAction
    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_ GoogleCloudAiplatformV1ProbeExecAction
    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'.

    GoogleCloudAiplatformV1ProbeExecAction, GoogleCloudAiplatformV1ProbeExecActionArgs

    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.

    GoogleCloudAiplatformV1ProbeExecActionResponse, GoogleCloudAiplatformV1ProbeExecActionResponseArgs

    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.

    GoogleCloudAiplatformV1ProbeResponse, GoogleCloudAiplatformV1ProbeResponseArgs

    Exec Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ProbeExecActionResponse
    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 GoogleCloudAiplatformV1ProbeExecActionResponse
    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 GoogleCloudAiplatformV1ProbeExecActionResponse
    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 GoogleCloudAiplatformV1ProbeExecActionResponse
    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_ GoogleCloudAiplatformV1ProbeExecActionResponse
    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'.

    GoogleCloudAiplatformV1SampledShapleyAttribution, GoogleCloudAiplatformV1SampledShapleyAttributionArgs

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

    GoogleCloudAiplatformV1SampledShapleyAttributionResponse, GoogleCloudAiplatformV1SampledShapleyAttributionResponseArgs

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

    GoogleCloudAiplatformV1SmoothGradConfig, GoogleCloudAiplatformV1SmoothGradConfigArgs

    FeatureNoiseSigma Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FeatureNoiseSigma
    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 GoogleCloudAiplatformV1FeatureNoiseSigma
    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 GoogleCloudAiplatformV1FeatureNoiseSigma
    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 GoogleCloudAiplatformV1FeatureNoiseSigma
    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 GoogleCloudAiplatformV1FeatureNoiseSigma
    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.

    GoogleCloudAiplatformV1SmoothGradConfigResponse, GoogleCloudAiplatformV1SmoothGradConfigResponseArgs

    FeatureNoiseSigma Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1FeatureNoiseSigmaResponse
    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 GoogleCloudAiplatformV1FeatureNoiseSigmaResponse
    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 GoogleCloudAiplatformV1FeatureNoiseSigmaResponse
    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 GoogleCloudAiplatformV1FeatureNoiseSigmaResponse
    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 GoogleCloudAiplatformV1FeatureNoiseSigmaResponse
    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.

    GoogleCloudAiplatformV1StratifiedSplit, GoogleCloudAiplatformV1StratifiedSplitArgs

    Key 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 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 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 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 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 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.

    GoogleCloudAiplatformV1StratifiedSplitResponse, GoogleCloudAiplatformV1StratifiedSplitResponseArgs

    Key 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 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 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 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 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 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.

    GoogleCloudAiplatformV1TimestampSplit, GoogleCloudAiplatformV1TimestampSplitArgs

    Key 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 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 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 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 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 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.

    GoogleCloudAiplatformV1TimestampSplitResponse, GoogleCloudAiplatformV1TimestampSplitResponseArgs

    Key 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 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 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 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 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 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.

    GoogleCloudAiplatformV1XraiAttribution, GoogleCloudAiplatformV1XraiAttributionArgs

    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    BlurBaselineConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BlurBaselineConfig
    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.V1.Inputs.GoogleCloudAiplatformV1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    BlurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfig
    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 GoogleCloudAiplatformV1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Integer
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfig
    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 GoogleCloudAiplatformV1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfig
    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 GoogleCloudAiplatformV1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    step_count int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blur_baseline_config GoogleCloudAiplatformV1BlurBaselineConfig
    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 GoogleCloudAiplatformV1SmoothGradConfig
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig Property Map
    Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig Property Map
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf

    GoogleCloudAiplatformV1XraiAttributionResponse, GoogleCloudAiplatformV1XraiAttributionResponseArgs

    BlurBaselineConfig Pulumi.GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1BlurBaselineConfigResponse
    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.V1.Inputs.GoogleCloudAiplatformV1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    BlurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfigResponse
    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 GoogleCloudAiplatformV1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    StepCount int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfigResponse
    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 GoogleCloudAiplatformV1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Integer
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig GoogleCloudAiplatformV1BlurBaselineConfigResponse
    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 GoogleCloudAiplatformV1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blur_baseline_config GoogleCloudAiplatformV1BlurBaselineConfigResponse
    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 GoogleCloudAiplatformV1SmoothGradConfigResponse
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    step_count int
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.
    blurBaselineConfig Property Map
    Config for XRAI with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383
    smoothGradConfig Property Map
    Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
    stepCount Number
    The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is met within the desired error range. Valid range of its value is [1, 100], inclusively.

    GoogleRpcStatusResponse, GoogleRpcStatusResponseArgs

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

    Package Details

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

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

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