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Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
google-native.aiplatform/v1beta1.TrainingPipeline
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Google Cloud Native is in preview. Google Cloud Classic is fully supported.
Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi
Creates a TrainingPipeline. A created TrainingPipeline right away will be attempted to be run. Auto-naming is currently not supported for this resource.
Create TrainingPipeline Resource
Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.
Constructor syntax
new TrainingPipeline(name: string, args: TrainingPipelineArgs, opts?: CustomResourceOptions);@overload
def TrainingPipeline(resource_name: str,
args: TrainingPipelineArgs,
opts: Optional[ResourceOptions] = None)
@overload
def TrainingPipeline(resource_name: str,
opts: Optional[ResourceOptions] = None,
display_name: Optional[str] = None,
training_task_definition: Optional[str] = None,
training_task_inputs: Optional[Any] = None,
encryption_spec: Optional[GoogleCloudAiplatformV1beta1EncryptionSpecArgs] = None,
input_data_config: Optional[GoogleCloudAiplatformV1beta1InputDataConfigArgs] = None,
labels: Optional[Mapping[str, str]] = None,
location: Optional[str] = None,
model_id: Optional[str] = None,
model_to_upload: Optional[GoogleCloudAiplatformV1beta1ModelArgs] = None,
parent_model: Optional[str] = None,
project: Optional[str] = None)func NewTrainingPipeline(ctx *Context, name string, args TrainingPipelineArgs, opts ...ResourceOption) (*TrainingPipeline, error)public TrainingPipeline(string name, TrainingPipelineArgs args, CustomResourceOptions? opts = null)
public TrainingPipeline(String name, TrainingPipelineArgs args)
public TrainingPipeline(String name, TrainingPipelineArgs args, CustomResourceOptions options)
type: google-native:aiplatform/v1beta1:TrainingPipeline
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.
Parameters
- name 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 google_nativeTrainingPipelineResource = new GoogleNative.Aiplatform.V1Beta1.TrainingPipeline("google-nativeTrainingPipelineResource", new()
{
DisplayName = "string",
TrainingTaskDefinition = "string",
TrainingTaskInputs = "any",
EncryptionSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EncryptionSpecArgs
{
KmsKeyName = "string",
},
InputDataConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1InputDataConfigArgs
{
DatasetId = "string",
AnnotationSchemaUri = "string",
AnnotationsFilter = "string",
BigqueryDestination = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BigQueryDestinationArgs
{
OutputUri = "string",
},
FilterSplit = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FilterSplitArgs
{
TestFilter = "string",
TrainingFilter = "string",
ValidationFilter = "string",
},
FractionSplit = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FractionSplitArgs
{
TestFraction = 0,
TrainingFraction = 0,
ValidationFraction = 0,
},
GcsDestination = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationArgs
{
OutputUriPrefix = "string",
},
PersistMlUseAssignment = false,
PredefinedSplit = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PredefinedSplitArgs
{
Key = "string",
},
SavedQueryId = "string",
StratifiedSplit = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1StratifiedSplitArgs
{
Key = "string",
TestFraction = 0,
TrainingFraction = 0,
ValidationFraction = 0,
},
TimestampSplit = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1TimestampSplitArgs
{
Key = "string",
TestFraction = 0,
TrainingFraction = 0,
ValidationFraction = 0,
},
},
Labels =
{
{ "string", "string" },
},
Location = "string",
ModelId = "string",
ModelToUpload = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelArgs
{
DisplayName = "string",
ExplanationSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationSpecArgs
{
Parameters = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationParametersArgs
{
Examples = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExamplesArgs
{
ExampleGcsSource = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceArgs
{
DataFormat = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormat.DataFormatUnspecified,
GcsSource = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceArgs
{
Uris = new[]
{
"string",
},
},
},
GcsSource = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsSourceArgs
{
Uris = new[]
{
"string",
},
},
NearestNeighborSearchConfig = "any",
NeighborCount = 0,
Presets = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PresetsArgs
{
Modality = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1PresetsModality.ModalityUnspecified,
Query = GoogleNative.Aiplatform.V1Beta1.GoogleCloudAiplatformV1beta1PresetsQuery.Precise,
},
},
IntegratedGradientsAttribution = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionArgs
{
StepCount = 0,
BlurBaselineConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs
{
MaxBlurSigma = 0,
},
SmoothGradConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SmoothGradConfigArgs
{
FeatureNoiseSigma = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs
{
NoiseSigma = new[]
{
new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs
{
Name = "string",
Sigma = 0,
},
},
},
NoiseSigma = 0,
NoisySampleCount = 0,
},
},
OutputIndices = new[]
{
"any",
},
SampledShapleyAttribution = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SampledShapleyAttributionArgs
{
PathCount = 0,
},
TopK = 0,
XraiAttribution = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1XraiAttributionArgs
{
StepCount = 0,
BlurBaselineConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs
{
MaxBlurSigma = 0,
},
SmoothGradConfig = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SmoothGradConfigArgs
{
FeatureNoiseSigma = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs
{
NoiseSigma = new[]
{
new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs
{
Name = "string",
Sigma = 0,
},
},
},
NoiseSigma = 0,
NoisySampleCount = 0,
},
},
},
Metadata = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ExplanationMetadataArgs
{
Inputs =
{
{ "string", "string" },
},
Outputs =
{
{ "string", "string" },
},
FeatureAttributionsSchemaUri = "string",
LatentSpaceSource = "string",
},
},
Description = "string",
ContainerSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ModelContainerSpecArgs
{
ImageUri = "string",
Args = new[]
{
"string",
},
Command = new[]
{
"string",
},
DeploymentTimeout = "string",
Env = new[]
{
new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EnvVarArgs
{
Name = "string",
Value = "string",
},
},
HealthProbe = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeArgs
{
Exec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeExecActionArgs
{
Command = new[]
{
"string",
},
},
PeriodSeconds = 0,
TimeoutSeconds = 0,
},
HealthRoute = "string",
Ports = new[]
{
new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PortArgs
{
ContainerPort = 0,
},
},
PredictRoute = "string",
SharedMemorySizeMb = "string",
StartupProbe = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeArgs
{
Exec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ProbeExecActionArgs
{
Command = new[]
{
"string",
},
},
PeriodSeconds = 0,
TimeoutSeconds = 0,
},
},
EncryptionSpec = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EncryptionSpecArgs
{
KmsKeyName = "string",
},
Etag = "string",
ArtifactUri = "string",
Labels =
{
{ "string", "string" },
},
Metadata = "any",
MetadataSchemaUri = "string",
Name = "string",
PredictSchemata = new GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PredictSchemataArgs
{
InstanceSchemaUri = "string",
ParametersSchemaUri = "string",
PredictionSchemaUri = "string",
},
VersionAliases = new[]
{
"string",
},
VersionDescription = "string",
},
ParentModel = "string",
Project = "string",
});
example, err := aiplatformv1beta1.NewTrainingPipeline(ctx, "google-nativeTrainingPipelineResource", &aiplatformv1beta1.TrainingPipelineArgs{
DisplayName: pulumi.String("string"),
TrainingTaskDefinition: pulumi.String("string"),
TrainingTaskInputs: pulumi.Any("any"),
EncryptionSpec: &aiplatform.GoogleCloudAiplatformV1beta1EncryptionSpecArgs{
KmsKeyName: pulumi.String("string"),
},
InputDataConfig: &aiplatform.GoogleCloudAiplatformV1beta1InputDataConfigArgs{
DatasetId: pulumi.String("string"),
AnnotationSchemaUri: pulumi.String("string"),
AnnotationsFilter: pulumi.String("string"),
BigqueryDestination: &aiplatform.GoogleCloudAiplatformV1beta1BigQueryDestinationArgs{
OutputUri: pulumi.String("string"),
},
FilterSplit: &aiplatform.GoogleCloudAiplatformV1beta1FilterSplitArgs{
TestFilter: pulumi.String("string"),
TrainingFilter: pulumi.String("string"),
ValidationFilter: pulumi.String("string"),
},
FractionSplit: &aiplatform.GoogleCloudAiplatformV1beta1FractionSplitArgs{
TestFraction: pulumi.Float64(0),
TrainingFraction: pulumi.Float64(0),
ValidationFraction: pulumi.Float64(0),
},
GcsDestination: &aiplatform.GoogleCloudAiplatformV1beta1GcsDestinationArgs{
OutputUriPrefix: pulumi.String("string"),
},
PersistMlUseAssignment: pulumi.Bool(false),
PredefinedSplit: &aiplatform.GoogleCloudAiplatformV1beta1PredefinedSplitArgs{
Key: pulumi.String("string"),
},
SavedQueryId: pulumi.String("string"),
StratifiedSplit: &aiplatform.GoogleCloudAiplatformV1beta1StratifiedSplitArgs{
Key: pulumi.String("string"),
TestFraction: pulumi.Float64(0),
TrainingFraction: pulumi.Float64(0),
ValidationFraction: pulumi.Float64(0),
},
TimestampSplit: &aiplatform.GoogleCloudAiplatformV1beta1TimestampSplitArgs{
Key: pulumi.String("string"),
TestFraction: pulumi.Float64(0),
TrainingFraction: pulumi.Float64(0),
ValidationFraction: pulumi.Float64(0),
},
},
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
Location: pulumi.String("string"),
ModelId: pulumi.String("string"),
ModelToUpload: &aiplatform.GoogleCloudAiplatformV1beta1ModelArgs{
DisplayName: pulumi.String("string"),
ExplanationSpec: &aiplatform.GoogleCloudAiplatformV1beta1ExplanationSpecArgs{
Parameters: &aiplatform.GoogleCloudAiplatformV1beta1ExplanationParametersArgs{
Examples: &aiplatform.GoogleCloudAiplatformV1beta1ExamplesArgs{
ExampleGcsSource: &aiplatform.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceArgs{
DataFormat: aiplatformv1beta1.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormatDataFormatUnspecified,
GcsSource: &aiplatform.GoogleCloudAiplatformV1beta1GcsSourceArgs{
Uris: pulumi.StringArray{
pulumi.String("string"),
},
},
},
GcsSource: &aiplatform.GoogleCloudAiplatformV1beta1GcsSourceArgs{
Uris: pulumi.StringArray{
pulumi.String("string"),
},
},
NearestNeighborSearchConfig: pulumi.Any("any"),
NeighborCount: pulumi.Int(0),
Presets: &aiplatform.GoogleCloudAiplatformV1beta1PresetsArgs{
Modality: aiplatformv1beta1.GoogleCloudAiplatformV1beta1PresetsModalityModalityUnspecified,
Query: aiplatformv1beta1.GoogleCloudAiplatformV1beta1PresetsQueryPrecise,
},
},
IntegratedGradientsAttribution: &aiplatform.GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionArgs{
StepCount: pulumi.Int(0),
BlurBaselineConfig: &aiplatform.GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs{
MaxBlurSigma: pulumi.Float64(0),
},
SmoothGradConfig: &aiplatform.GoogleCloudAiplatformV1beta1SmoothGradConfigArgs{
FeatureNoiseSigma: &aiplatform.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs{
NoiseSigma: aiplatform.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArray{
&aiplatform.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs{
Name: pulumi.String("string"),
Sigma: pulumi.Float64(0),
},
},
},
NoiseSigma: pulumi.Float64(0),
NoisySampleCount: pulumi.Int(0),
},
},
OutputIndices: pulumi.Array{
pulumi.Any("any"),
},
SampledShapleyAttribution: &aiplatform.GoogleCloudAiplatformV1beta1SampledShapleyAttributionArgs{
PathCount: pulumi.Int(0),
},
TopK: pulumi.Int(0),
XraiAttribution: &aiplatform.GoogleCloudAiplatformV1beta1XraiAttributionArgs{
StepCount: pulumi.Int(0),
BlurBaselineConfig: &aiplatform.GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs{
MaxBlurSigma: pulumi.Float64(0),
},
SmoothGradConfig: &aiplatform.GoogleCloudAiplatformV1beta1SmoothGradConfigArgs{
FeatureNoiseSigma: &aiplatform.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs{
NoiseSigma: aiplatform.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArray{
&aiplatform.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs{
Name: pulumi.String("string"),
Sigma: pulumi.Float64(0),
},
},
},
NoiseSigma: pulumi.Float64(0),
NoisySampleCount: pulumi.Int(0),
},
},
},
Metadata: &aiplatform.GoogleCloudAiplatformV1beta1ExplanationMetadataArgs{
Inputs: pulumi.StringMap{
"string": pulumi.String("string"),
},
Outputs: pulumi.StringMap{
"string": pulumi.String("string"),
},
FeatureAttributionsSchemaUri: pulumi.String("string"),
LatentSpaceSource: pulumi.String("string"),
},
},
Description: pulumi.String("string"),
ContainerSpec: &aiplatform.GoogleCloudAiplatformV1beta1ModelContainerSpecArgs{
ImageUri: pulumi.String("string"),
Args: pulumi.StringArray{
pulumi.String("string"),
},
Command: pulumi.StringArray{
pulumi.String("string"),
},
DeploymentTimeout: pulumi.String("string"),
Env: aiplatform.GoogleCloudAiplatformV1beta1EnvVarArray{
&aiplatform.GoogleCloudAiplatformV1beta1EnvVarArgs{
Name: pulumi.String("string"),
Value: pulumi.String("string"),
},
},
HealthProbe: &aiplatform.GoogleCloudAiplatformV1beta1ProbeArgs{
Exec: &aiplatform.GoogleCloudAiplatformV1beta1ProbeExecActionArgs{
Command: pulumi.StringArray{
pulumi.String("string"),
},
},
PeriodSeconds: pulumi.Int(0),
TimeoutSeconds: pulumi.Int(0),
},
HealthRoute: pulumi.String("string"),
Ports: aiplatform.GoogleCloudAiplatformV1beta1PortArray{
&aiplatform.GoogleCloudAiplatformV1beta1PortArgs{
ContainerPort: pulumi.Int(0),
},
},
PredictRoute: pulumi.String("string"),
SharedMemorySizeMb: pulumi.String("string"),
StartupProbe: &aiplatform.GoogleCloudAiplatformV1beta1ProbeArgs{
Exec: &aiplatform.GoogleCloudAiplatformV1beta1ProbeExecActionArgs{
Command: pulumi.StringArray{
pulumi.String("string"),
},
},
PeriodSeconds: pulumi.Int(0),
TimeoutSeconds: pulumi.Int(0),
},
},
EncryptionSpec: &aiplatform.GoogleCloudAiplatformV1beta1EncryptionSpecArgs{
KmsKeyName: pulumi.String("string"),
},
Etag: pulumi.String("string"),
ArtifactUri: pulumi.String("string"),
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
Metadata: pulumi.Any("any"),
MetadataSchemaUri: pulumi.String("string"),
Name: pulumi.String("string"),
PredictSchemata: &aiplatform.GoogleCloudAiplatformV1beta1PredictSchemataArgs{
InstanceSchemaUri: pulumi.String("string"),
ParametersSchemaUri: pulumi.String("string"),
PredictionSchemaUri: pulumi.String("string"),
},
VersionAliases: pulumi.StringArray{
pulumi.String("string"),
},
VersionDescription: pulumi.String("string"),
},
ParentModel: pulumi.String("string"),
Project: pulumi.String("string"),
})
var google_nativeTrainingPipelineResource = new TrainingPipeline("google-nativeTrainingPipelineResource", TrainingPipelineArgs.builder()
.displayName("string")
.trainingTaskDefinition("string")
.trainingTaskInputs("any")
.encryptionSpec(GoogleCloudAiplatformV1beta1EncryptionSpecArgs.builder()
.kmsKeyName("string")
.build())
.inputDataConfig(GoogleCloudAiplatformV1beta1InputDataConfigArgs.builder()
.datasetId("string")
.annotationSchemaUri("string")
.annotationsFilter("string")
.bigqueryDestination(GoogleCloudAiplatformV1beta1BigQueryDestinationArgs.builder()
.outputUri("string")
.build())
.filterSplit(GoogleCloudAiplatformV1beta1FilterSplitArgs.builder()
.testFilter("string")
.trainingFilter("string")
.validationFilter("string")
.build())
.fractionSplit(GoogleCloudAiplatformV1beta1FractionSplitArgs.builder()
.testFraction(0)
.trainingFraction(0)
.validationFraction(0)
.build())
.gcsDestination(GoogleCloudAiplatformV1beta1GcsDestinationArgs.builder()
.outputUriPrefix("string")
.build())
.persistMlUseAssignment(false)
.predefinedSplit(GoogleCloudAiplatformV1beta1PredefinedSplitArgs.builder()
.key("string")
.build())
.savedQueryId("string")
.stratifiedSplit(GoogleCloudAiplatformV1beta1StratifiedSplitArgs.builder()
.key("string")
.testFraction(0)
.trainingFraction(0)
.validationFraction(0)
.build())
.timestampSplit(GoogleCloudAiplatformV1beta1TimestampSplitArgs.builder()
.key("string")
.testFraction(0)
.trainingFraction(0)
.validationFraction(0)
.build())
.build())
.labels(Map.of("string", "string"))
.location("string")
.modelId("string")
.modelToUpload(GoogleCloudAiplatformV1beta1ModelArgs.builder()
.displayName("string")
.explanationSpec(GoogleCloudAiplatformV1beta1ExplanationSpecArgs.builder()
.parameters(GoogleCloudAiplatformV1beta1ExplanationParametersArgs.builder()
.examples(GoogleCloudAiplatformV1beta1ExamplesArgs.builder()
.exampleGcsSource(GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceArgs.builder()
.dataFormat("DATA_FORMAT_UNSPECIFIED")
.gcsSource(GoogleCloudAiplatformV1beta1GcsSourceArgs.builder()
.uris("string")
.build())
.build())
.gcsSource(GoogleCloudAiplatformV1beta1GcsSourceArgs.builder()
.uris("string")
.build())
.nearestNeighborSearchConfig("any")
.neighborCount(0)
.presets(GoogleCloudAiplatformV1beta1PresetsArgs.builder()
.modality("MODALITY_UNSPECIFIED")
.query("PRECISE")
.build())
.build())
.integratedGradientsAttribution(GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionArgs.builder()
.stepCount(0)
.blurBaselineConfig(GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs.builder()
.maxBlurSigma(0)
.build())
.smoothGradConfig(GoogleCloudAiplatformV1beta1SmoothGradConfigArgs.builder()
.featureNoiseSigma(GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs.builder()
.noiseSigma(GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs.builder()
.name("string")
.sigma(0)
.build())
.build())
.noiseSigma(0)
.noisySampleCount(0)
.build())
.build())
.outputIndices("any")
.sampledShapleyAttribution(GoogleCloudAiplatformV1beta1SampledShapleyAttributionArgs.builder()
.pathCount(0)
.build())
.topK(0)
.xraiAttribution(GoogleCloudAiplatformV1beta1XraiAttributionArgs.builder()
.stepCount(0)
.blurBaselineConfig(GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs.builder()
.maxBlurSigma(0)
.build())
.smoothGradConfig(GoogleCloudAiplatformV1beta1SmoothGradConfigArgs.builder()
.featureNoiseSigma(GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs.builder()
.noiseSigma(GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs.builder()
.name("string")
.sigma(0)
.build())
.build())
.noiseSigma(0)
.noisySampleCount(0)
.build())
.build())
.build())
.metadata(GoogleCloudAiplatformV1beta1ExplanationMetadataArgs.builder()
.inputs(Map.of("string", "string"))
.outputs(Map.of("string", "string"))
.featureAttributionsSchemaUri("string")
.latentSpaceSource("string")
.build())
.build())
.description("string")
.containerSpec(GoogleCloudAiplatformV1beta1ModelContainerSpecArgs.builder()
.imageUri("string")
.args("string")
.command("string")
.deploymentTimeout("string")
.env(GoogleCloudAiplatformV1beta1EnvVarArgs.builder()
.name("string")
.value("string")
.build())
.healthProbe(GoogleCloudAiplatformV1beta1ProbeArgs.builder()
.exec(GoogleCloudAiplatformV1beta1ProbeExecActionArgs.builder()
.command("string")
.build())
.periodSeconds(0)
.timeoutSeconds(0)
.build())
.healthRoute("string")
.ports(GoogleCloudAiplatformV1beta1PortArgs.builder()
.containerPort(0)
.build())
.predictRoute("string")
.sharedMemorySizeMb("string")
.startupProbe(GoogleCloudAiplatformV1beta1ProbeArgs.builder()
.exec(GoogleCloudAiplatformV1beta1ProbeExecActionArgs.builder()
.command("string")
.build())
.periodSeconds(0)
.timeoutSeconds(0)
.build())
.build())
.encryptionSpec(GoogleCloudAiplatformV1beta1EncryptionSpecArgs.builder()
.kmsKeyName("string")
.build())
.etag("string")
.artifactUri("string")
.labels(Map.of("string", "string"))
.metadata("any")
.metadataSchemaUri("string")
.name("string")
.predictSchemata(GoogleCloudAiplatformV1beta1PredictSchemataArgs.builder()
.instanceSchemaUri("string")
.parametersSchemaUri("string")
.predictionSchemaUri("string")
.build())
.versionAliases("string")
.versionDescription("string")
.build())
.parentModel("string")
.project("string")
.build());
google_native_training_pipeline_resource = google_native.aiplatform.v1beta1.TrainingPipeline("google-nativeTrainingPipelineResource",
display_name="string",
training_task_definition="string",
training_task_inputs="any",
encryption_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1EncryptionSpecArgs(
kms_key_name="string",
),
input_data_config=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1InputDataConfigArgs(
dataset_id="string",
annotation_schema_uri="string",
annotations_filter="string",
bigquery_destination=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1BigQueryDestinationArgs(
output_uri="string",
),
filter_split=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FilterSplitArgs(
test_filter="string",
training_filter="string",
validation_filter="string",
),
fraction_split=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FractionSplitArgs(
test_fraction=0,
training_fraction=0,
validation_fraction=0,
),
gcs_destination=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1GcsDestinationArgs(
output_uri_prefix="string",
),
persist_ml_use_assignment=False,
predefined_split=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1PredefinedSplitArgs(
key="string",
),
saved_query_id="string",
stratified_split=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1StratifiedSplitArgs(
key="string",
test_fraction=0,
training_fraction=0,
validation_fraction=0,
),
timestamp_split=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1TimestampSplitArgs(
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.v1beta1.GoogleCloudAiplatformV1beta1ModelArgs(
display_name="string",
explanation_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ExplanationSpecArgs(
parameters=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ExplanationParametersArgs(
examples=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ExamplesArgs(
example_gcs_source=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceArgs(
data_format=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormat.DATA_FORMAT_UNSPECIFIED,
gcs_source=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1GcsSourceArgs(
uris=["string"],
),
),
gcs_source=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1GcsSourceArgs(
uris=["string"],
),
nearest_neighbor_search_config="any",
neighbor_count=0,
presets=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1PresetsArgs(
modality=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1PresetsModality.MODALITY_UNSPECIFIED,
query=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1PresetsQuery.PRECISE,
),
),
integrated_gradients_attribution=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionArgs(
step_count=0,
blur_baseline_config=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs(
max_blur_sigma=0,
),
smooth_grad_config=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1SmoothGradConfigArgs(
feature_noise_sigma=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs(
noise_sigma=[google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs(
name="string",
sigma=0,
)],
),
noise_sigma=0,
noisy_sample_count=0,
),
),
output_indices=["any"],
sampled_shapley_attribution=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1SampledShapleyAttributionArgs(
path_count=0,
),
top_k=0,
xrai_attribution=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1XraiAttributionArgs(
step_count=0,
blur_baseline_config=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs(
max_blur_sigma=0,
),
smooth_grad_config=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1SmoothGradConfigArgs(
feature_noise_sigma=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs(
noise_sigma=[google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs(
name="string",
sigma=0,
)],
),
noise_sigma=0,
noisy_sample_count=0,
),
),
),
metadata=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ExplanationMetadataArgs(
inputs={
"string": "string",
},
outputs={
"string": "string",
},
feature_attributions_schema_uri="string",
latent_space_source="string",
),
),
description="string",
container_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ModelContainerSpecArgs(
image_uri="string",
args=["string"],
command=["string"],
deployment_timeout="string",
env=[google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1EnvVarArgs(
name="string",
value="string",
)],
health_probe=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ProbeArgs(
exec_=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ProbeExecActionArgs(
command=["string"],
),
period_seconds=0,
timeout_seconds=0,
),
health_route="string",
ports=[google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1PortArgs(
container_port=0,
)],
predict_route="string",
shared_memory_size_mb="string",
startup_probe=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ProbeArgs(
exec_=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ProbeExecActionArgs(
command=["string"],
),
period_seconds=0,
timeout_seconds=0,
),
),
encryption_spec=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1EncryptionSpecArgs(
kms_key_name="string",
),
etag="string",
artifact_uri="string",
labels={
"string": "string",
},
metadata="any",
metadata_schema_uri="string",
name="string",
predict_schemata=google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1PredictSchemataArgs(
instance_schema_uri="string",
parameters_schema_uri="string",
prediction_schema_uri="string",
),
version_aliases=["string"],
version_description="string",
),
parent_model="string",
project="string")
const google_nativeTrainingPipelineResource = new google_native.aiplatform.v1beta1.TrainingPipeline("google-nativeTrainingPipelineResource", {
displayName: "string",
trainingTaskDefinition: "string",
trainingTaskInputs: "any",
encryptionSpec: {
kmsKeyName: "string",
},
inputDataConfig: {
datasetId: "string",
annotationSchemaUri: "string",
annotationsFilter: "string",
bigqueryDestination: {
outputUri: "string",
},
filterSplit: {
testFilter: "string",
trainingFilter: "string",
validationFilter: "string",
},
fractionSplit: {
testFraction: 0,
trainingFraction: 0,
validationFraction: 0,
},
gcsDestination: {
outputUriPrefix: "string",
},
persistMlUseAssignment: false,
predefinedSplit: {
key: "string",
},
savedQueryId: "string",
stratifiedSplit: {
key: "string",
testFraction: 0,
trainingFraction: 0,
validationFraction: 0,
},
timestampSplit: {
key: "string",
testFraction: 0,
trainingFraction: 0,
validationFraction: 0,
},
},
labels: {
string: "string",
},
location: "string",
modelId: "string",
modelToUpload: {
displayName: "string",
explanationSpec: {
parameters: {
examples: {
exampleGcsSource: {
dataFormat: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormat.DataFormatUnspecified,
gcsSource: {
uris: ["string"],
},
},
gcsSource: {
uris: ["string"],
},
nearestNeighborSearchConfig: "any",
neighborCount: 0,
presets: {
modality: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1PresetsModality.ModalityUnspecified,
query: google_native.aiplatform.v1beta1.GoogleCloudAiplatformV1beta1PresetsQuery.Precise,
},
},
integratedGradientsAttribution: {
stepCount: 0,
blurBaselineConfig: {
maxBlurSigma: 0,
},
smoothGradConfig: {
featureNoiseSigma: {
noiseSigma: [{
name: "string",
sigma: 0,
}],
},
noiseSigma: 0,
noisySampleCount: 0,
},
},
outputIndices: ["any"],
sampledShapleyAttribution: {
pathCount: 0,
},
topK: 0,
xraiAttribution: {
stepCount: 0,
blurBaselineConfig: {
maxBlurSigma: 0,
},
smoothGradConfig: {
featureNoiseSigma: {
noiseSigma: [{
name: "string",
sigma: 0,
}],
},
noiseSigma: 0,
noisySampleCount: 0,
},
},
},
metadata: {
inputs: {
string: "string",
},
outputs: {
string: "string",
},
featureAttributionsSchemaUri: "string",
latentSpaceSource: "string",
},
},
description: "string",
containerSpec: {
imageUri: "string",
args: ["string"],
command: ["string"],
deploymentTimeout: "string",
env: [{
name: "string",
value: "string",
}],
healthProbe: {
exec: {
command: ["string"],
},
periodSeconds: 0,
timeoutSeconds: 0,
},
healthRoute: "string",
ports: [{
containerPort: 0,
}],
predictRoute: "string",
sharedMemorySizeMb: "string",
startupProbe: {
exec: {
command: ["string"],
},
periodSeconds: 0,
timeoutSeconds: 0,
},
},
encryptionSpec: {
kmsKeyName: "string",
},
etag: "string",
artifactUri: "string",
labels: {
string: "string",
},
metadata: "any",
metadataSchemaUri: "string",
name: "string",
predictSchemata: {
instanceSchemaUri: "string",
parametersSchemaUri: "string",
predictionSchemaUri: "string",
},
versionAliases: ["string"],
versionDescription: "string",
},
parentModel: "string",
project: "string",
});
type: google-native:aiplatform/v1beta1:TrainingPipeline
properties:
displayName: string
encryptionSpec:
kmsKeyName: string
inputDataConfig:
annotationSchemaUri: string
annotationsFilter: string
bigqueryDestination:
outputUri: string
datasetId: string
filterSplit:
testFilter: string
trainingFilter: string
validationFilter: string
fractionSplit:
testFraction: 0
trainingFraction: 0
validationFraction: 0
gcsDestination:
outputUriPrefix: string
persistMlUseAssignment: false
predefinedSplit:
key: string
savedQueryId: string
stratifiedSplit:
key: string
testFraction: 0
trainingFraction: 0
validationFraction: 0
timestampSplit:
key: string
testFraction: 0
trainingFraction: 0
validationFraction: 0
labels:
string: string
location: string
modelId: string
modelToUpload:
artifactUri: string
containerSpec:
args:
- string
command:
- string
deploymentTimeout: string
env:
- name: string
value: string
healthProbe:
exec:
command:
- string
periodSeconds: 0
timeoutSeconds: 0
healthRoute: string
imageUri: string
ports:
- containerPort: 0
predictRoute: string
sharedMemorySizeMb: string
startupProbe:
exec:
command:
- string
periodSeconds: 0
timeoutSeconds: 0
description: string
displayName: string
encryptionSpec:
kmsKeyName: string
etag: string
explanationSpec:
metadata:
featureAttributionsSchemaUri: string
inputs:
string: string
latentSpaceSource: string
outputs:
string: string
parameters:
examples:
exampleGcsSource:
dataFormat: DATA_FORMAT_UNSPECIFIED
gcsSource:
uris:
- string
gcsSource:
uris:
- string
nearestNeighborSearchConfig: any
neighborCount: 0
presets:
modality: MODALITY_UNSPECIFIED
query: PRECISE
integratedGradientsAttribution:
blurBaselineConfig:
maxBlurSigma: 0
smoothGradConfig:
featureNoiseSigma:
noiseSigma:
- name: string
sigma: 0
noiseSigma: 0
noisySampleCount: 0
stepCount: 0
outputIndices:
- any
sampledShapleyAttribution:
pathCount: 0
topK: 0
xraiAttribution:
blurBaselineConfig:
maxBlurSigma: 0
smoothGradConfig:
featureNoiseSigma:
noiseSigma:
- name: string
sigma: 0
noiseSigma: 0
noisySampleCount: 0
stepCount: 0
labels:
string: string
metadata: any
metadataSchemaUri: string
name: string
predictSchemata:
instanceSchemaUri: string
parametersSchemaUri: string
predictionSchemaUri: string
versionAliases:
- string
versionDescription: string
parentModel: string
project: string
trainingTaskDefinition: string
trainingTaskInputs: any
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:
- Display
Name string - The user-defined name of this TrainingPipeline.
- Training
Task stringDefinition - 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 objectInputs - The training task's parameter(s), as specified in the training_task_definition's
inputs. - Encryption
Spec Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Encryption Spec - 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 Pulumi.Config Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Input Data Config - 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
- Model
Id 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. - Model
To Pulumi.Upload Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Model - 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_SUCCEEDEDand 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 string - Optional. When specify this field, the
model_to_uploadwill not be uploaded as a new model, instead, it will become a new version of thisparent_model. - Project string
- Display
Name string - The user-defined name of this TrainingPipeline.
- Training
Task stringDefinition - 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 interface{}Inputs - The training task's parameter(s), as specified in the training_task_definition's
inputs. - Encryption
Spec GoogleCloud Aiplatform V1beta1Encryption Spec Args - 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 GoogleConfig Cloud Aiplatform V1beta1Input Data Config Args - 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
- Model
Id 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. - Model
To GoogleUpload Cloud Aiplatform V1beta1Model Args - 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_SUCCEEDEDand 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 string - Optional. When specify this field, the
model_to_uploadwill not be uploaded as a new model, instead, it will become a new version of thisparent_model. - Project string
- display
Name String - The user-defined name of this TrainingPipeline.
- training
Task StringDefinition - 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 ObjectInputs - The training task's parameter(s), as specified in the training_task_definition's
inputs. - encryption
Spec GoogleCloud Aiplatform V1beta1Encryption Spec - 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 GoogleConfig Cloud Aiplatform V1beta1Input Data Config - 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
- model
Id 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. - model
To GoogleUpload Cloud Aiplatform V1beta1Model - 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_SUCCEEDEDand 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 String - Optional. When specify this field, the
model_to_uploadwill not be uploaded as a new model, instead, it will become a new version of thisparent_model. - project String
- display
Name string - The user-defined name of this TrainingPipeline.
- training
Task stringDefinition - 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 anyInputs - The training task's parameter(s), as specified in the training_task_definition's
inputs. - encryption
Spec GoogleCloud Aiplatform V1beta1Encryption Spec - 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 GoogleConfig Cloud Aiplatform V1beta1Input Data Config - 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
- model
Id 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. - model
To GoogleUpload Cloud Aiplatform V1beta1Model - 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_SUCCEEDEDand 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 string - Optional. When specify this field, the
model_to_uploadwill not be uploaded as a new model, instead, it will become a new version of thisparent_model. - project string
- display_
name str - The user-defined name of this TrainingPipeline.
- training_
task_ strdefinition - 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_ Anyinputs - The training task's parameter(s), as specified in the training_task_definition's
inputs. - encryption_
spec GoogleCloud Aiplatform V1beta1Encryption Spec Args - 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_ Googleconfig Cloud Aiplatform V1beta1Input Data Config Args - 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_ Googleupload Cloud Aiplatform V1beta1Model Args - 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_SUCCEEDEDand 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_uploadwill not be uploaded as a new model, instead, it will become a new version of thisparent_model. - project str
- display
Name String - The user-defined name of this TrainingPipeline.
- training
Task StringDefinition - 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 AnyInputs - The training task's parameter(s), as specified in the training_task_definition's
inputs. - encryption
Spec 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.
- input
Data Property MapConfig - 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
- model
Id 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. - model
To Property MapUpload - 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_SUCCEEDEDand 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 String - Optional. When specify this field, the
model_to_uploadwill not be uploaded as a new model, instead, it will become a new version of thisparent_model. - project String
Outputs
All input properties are implicitly available as output properties. Additionally, the TrainingPipeline resource produces the following output properties:
- Create
Time string - Time when the TrainingPipeline was created.
- End
Time string - Time when the TrainingPipeline entered any of the following states:
PIPELINE_STATE_SUCCEEDED,PIPELINE_STATE_FAILED,PIPELINE_STATE_CANCELLED. - Error
Pulumi.
Google Native. Aiplatform. V1Beta1. Outputs. Google Rpc Status Response - Only populated when the pipeline's state is
PIPELINE_STATE_FAILEDorPIPELINE_STATE_CANCELLED. - Id string
- The provider-assigned unique ID for this managed resource.
- Name string
- Resource name of the TrainingPipeline.
- Start
Time string - Time when the TrainingPipeline for the first time entered the
PIPELINE_STATE_RUNNINGstate. - State string
- The detailed state of the pipeline.
- Training
Task objectMetadata - 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 containsmetadataobject. - Update
Time string - Time when the TrainingPipeline was most recently updated.
- Create
Time string - Time when the TrainingPipeline was created.
- End
Time string - Time when the TrainingPipeline entered any of the following states:
PIPELINE_STATE_SUCCEEDED,PIPELINE_STATE_FAILED,PIPELINE_STATE_CANCELLED. - Error
Google
Rpc Status Response - Only populated when the pipeline's state is
PIPELINE_STATE_FAILEDorPIPELINE_STATE_CANCELLED. - Id string
- The provider-assigned unique ID for this managed resource.
- Name string
- Resource name of the TrainingPipeline.
- Start
Time string - Time when the TrainingPipeline for the first time entered the
PIPELINE_STATE_RUNNINGstate. - State string
- The detailed state of the pipeline.
- Training
Task interface{}Metadata - 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 containsmetadataobject. - Update
Time string - Time when the TrainingPipeline was most recently updated.
- create
Time String - Time when the TrainingPipeline was created.
- end
Time String - Time when the TrainingPipeline entered any of the following states:
PIPELINE_STATE_SUCCEEDED,PIPELINE_STATE_FAILED,PIPELINE_STATE_CANCELLED. - error
Google
Rpc Status Response - Only populated when the pipeline's state is
PIPELINE_STATE_FAILEDorPIPELINE_STATE_CANCELLED. - id String
- The provider-assigned unique ID for this managed resource.
- name String
- Resource name of the TrainingPipeline.
- start
Time String - Time when the TrainingPipeline for the first time entered the
PIPELINE_STATE_RUNNINGstate. - state String
- The detailed state of the pipeline.
- training
Task ObjectMetadata - 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 containsmetadataobject. - update
Time String - Time when the TrainingPipeline was most recently updated.
- create
Time string - Time when the TrainingPipeline was created.
- end
Time string - Time when the TrainingPipeline entered any of the following states:
PIPELINE_STATE_SUCCEEDED,PIPELINE_STATE_FAILED,PIPELINE_STATE_CANCELLED. - error
Google
Rpc Status Response - Only populated when the pipeline's state is
PIPELINE_STATE_FAILEDorPIPELINE_STATE_CANCELLED. - id string
- The provider-assigned unique ID for this managed resource.
- name string
- Resource name of the TrainingPipeline.
- start
Time string - Time when the TrainingPipeline for the first time entered the
PIPELINE_STATE_RUNNINGstate. - state string
- The detailed state of the pipeline.
- training
Task anyMetadata - 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 containsmetadataobject. - update
Time 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
Google
Rpc Status Response - Only populated when the pipeline's state is
PIPELINE_STATE_FAILEDorPIPELINE_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_RUNNINGstate. - state str
- The detailed state of the pipeline.
- training_
task_ Anymetadata - 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 containsmetadataobject. - update_
time str - Time when the TrainingPipeline was most recently updated.
- create
Time String - Time when the TrainingPipeline was created.
- end
Time 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_FAILEDorPIPELINE_STATE_CANCELLED. - id String
- The provider-assigned unique ID for this managed resource.
- name String
- Resource name of the TrainingPipeline.
- start
Time String - Time when the TrainingPipeline for the first time entered the
PIPELINE_STATE_RUNNINGstate. - state String
- The detailed state of the pipeline.
- training
Task AnyMetadata - 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 containsmetadataobject. - update
Time String - Time when the TrainingPipeline was most recently updated.
Supporting Types
GoogleCloudAiplatformV1beta1BigQueryDestination, GoogleCloudAiplatformV1beta1BigQueryDestinationArgs
- Output
Uri 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://projectIdorbq://projectId.bqDatasetIdorbq://projectId.bqDatasetId.bqTableId.
- Output
Uri 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://projectIdorbq://projectId.bqDatasetIdorbq://projectId.bqDatasetId.bqTableId.
- output
Uri 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://projectIdorbq://projectId.bqDatasetIdorbq://projectId.bqDatasetId.bqTableId.
- output
Uri 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://projectIdorbq://projectId.bqDatasetIdorbq://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://projectIdorbq://projectId.bqDatasetIdorbq://projectId.bqDatasetId.bqTableId.
- output
Uri 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://projectIdorbq://projectId.bqDatasetIdorbq://projectId.bqDatasetId.bqTableId.
GoogleCloudAiplatformV1beta1BigQueryDestinationResponse, GoogleCloudAiplatformV1beta1BigQueryDestinationResponseArgs
- Output
Uri 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://projectIdorbq://projectId.bqDatasetIdorbq://projectId.bqDatasetId.bqTableId.
- Output
Uri 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://projectIdorbq://projectId.bqDatasetIdorbq://projectId.bqDatasetId.bqTableId.
- output
Uri 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://projectIdorbq://projectId.bqDatasetIdorbq://projectId.bqDatasetId.bqTableId.
- output
Uri 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://projectIdorbq://projectId.bqDatasetIdorbq://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://projectIdorbq://projectId.bqDatasetIdorbq://projectId.bqDatasetId.bqTableId.
- output
Uri 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://projectIdorbq://projectId.bqDatasetIdorbq://projectId.bqDatasetId.bqTableId.
GoogleCloudAiplatformV1beta1BlurBaselineConfig, GoogleCloudAiplatformV1beta1BlurBaselineConfigArgs
- Max
Blur doubleSigma - 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 float64Sigma - 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 DoubleSigma - 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 numberSigma - 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_ floatsigma - 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 NumberSigma - The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
GoogleCloudAiplatformV1beta1BlurBaselineConfigResponse, GoogleCloudAiplatformV1beta1BlurBaselineConfigResponseArgs
- Max
Blur doubleSigma - 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 float64Sigma - 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 DoubleSigma - 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 numberSigma - 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_ floatsigma - 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 NumberSigma - The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.
GoogleCloudAiplatformV1beta1DeployedModelRefResponse, GoogleCloudAiplatformV1beta1DeployedModelRefResponseArgs
- Deployed
Model stringId - Immutable. An ID of a DeployedModel in the above Endpoint.
- Endpoint string
- Immutable. A resource name of an Endpoint.
- Deployed
Model stringId - Immutable. An ID of a DeployedModel in the above Endpoint.
- Endpoint string
- Immutable. A resource name of an Endpoint.
- deployed
Model StringId - Immutable. An ID of a DeployedModel in the above Endpoint.
- endpoint String
- Immutable. A resource name of an Endpoint.
- deployed
Model stringId - Immutable. An ID of a DeployedModel in the above Endpoint.
- endpoint string
- Immutable. A resource name of an Endpoint.
- deployed_
model_ strid - Immutable. An ID of a DeployedModel in the above Endpoint.
- endpoint str
- Immutable. A resource name of an Endpoint.
- deployed
Model StringId - Immutable. An ID of a DeployedModel in the above Endpoint.
- endpoint String
- Immutable. A resource name of an Endpoint.
GoogleCloudAiplatformV1beta1EncryptionSpec, GoogleCloudAiplatformV1beta1EncryptionSpecArgs
- Kms
Key stringName - 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 stringName - 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 StringName - 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 stringName - 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_ strname - 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 StringName - The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form:
projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
GoogleCloudAiplatformV1beta1EncryptionSpecResponse, GoogleCloudAiplatformV1beta1EncryptionSpecResponseArgs
- Kms
Key stringName - 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 stringName - 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 StringName - 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 stringName - 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_ strname - 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 StringName - The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form:
projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
GoogleCloudAiplatformV1beta1EnvVar, GoogleCloudAiplatformV1beta1EnvVarArgs
- Name string
- Name of the environment variable. Must be a valid C identifier.
- Value string
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- Name string
- Name of the environment variable. Must be a valid C identifier.
- Value string
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name String
- Name of the environment variable. Must be a valid C identifier.
- value String
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name string
- Name of the environment variable. Must be a valid C identifier.
- value string
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name str
- Name of the environment variable. Must be a valid C identifier.
- value str
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name String
- Name of the environment variable. Must be a valid C identifier.
- value String
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
GoogleCloudAiplatformV1beta1EnvVarResponse, GoogleCloudAiplatformV1beta1EnvVarResponseArgs
- Name string
- Name of the environment variable. Must be a valid C identifier.
- Value string
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- Name string
- Name of the environment variable. Must be a valid C identifier.
- Value string
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name String
- Name of the environment variable. Must be a valid C identifier.
- value String
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name string
- Name of the environment variable. Must be a valid C identifier.
- value string
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name str
- Name of the environment variable. Must be a valid C identifier.
- value str
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name String
- Name of the environment variable. Must be a valid C identifier.
- value String
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
GoogleCloudAiplatformV1beta1Examples, GoogleCloudAiplatformV1beta1ExamplesArgs
- Example
Gcs Pulumi.Source Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Examples Example Gcs Source - The Cloud Storage input instances.
- Gcs
Source Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Gcs Source - The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
- Nearest
Neighbor objectSearch Config - 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
Pulumi.
Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Presets - Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
- Example
Gcs GoogleSource Cloud Aiplatform V1beta1Examples Example Gcs Source - The Cloud Storage input instances.
- Gcs
Source GoogleCloud Aiplatform V1beta1Gcs Source - The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
- Nearest
Neighbor interface{}Search Config - 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
Google
Cloud Aiplatform V1beta1Presets - Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
- example
Gcs GoogleSource Cloud Aiplatform V1beta1Examples Example Gcs Source - The Cloud Storage input instances.
- gcs
Source GoogleCloud Aiplatform V1beta1Gcs Source - The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
- nearest
Neighbor ObjectSearch Config - The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
- neighbor
Count Integer - The number of neighbors to return when querying for examples.
- presets
Google
Cloud Aiplatform V1beta1Presets - Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
- example
Gcs GoogleSource Cloud Aiplatform V1beta1Examples Example Gcs Source - The Cloud Storage input instances.
- gcs
Source GoogleCloud Aiplatform V1beta1Gcs Source - The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
- nearest
Neighbor anySearch Config - The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
- neighbor
Count number - The number of neighbors to return when querying for examples.
- presets
Google
Cloud Aiplatform V1beta1Presets - Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
- example_
gcs_ Googlesource Cloud Aiplatform V1beta1Examples Example Gcs Source - The Cloud Storage input instances.
- gcs_
source GoogleCloud Aiplatform V1beta1Gcs Source - The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
- nearest_
neighbor_ Anysearch_ config - 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
Google
Cloud Aiplatform V1beta1Presets - Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
- example
Gcs Property MapSource - The Cloud Storage input instances.
- gcs
Source Property Map - The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
- nearest
Neighbor AnySearch Config - The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
- neighbor
Count Number - The number of neighbors to return when querying for examples.
- presets Property Map
- Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
GoogleCloudAiplatformV1beta1ExamplesExampleGcsSource, GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceArgs
- Data
Format Pulumi.Google Native. Aiplatform. V1Beta1. Google Cloud Aiplatform V1beta1Examples Example Gcs Source Data Format - The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
- Gcs
Source Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Gcs Source - The Cloud Storage location for the input instances.
- Data
Format GoogleCloud Aiplatform V1beta1Examples Example Gcs Source Data Format - The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
- Gcs
Source GoogleCloud Aiplatform V1beta1Gcs Source - The Cloud Storage location for the input instances.
- data
Format GoogleCloud Aiplatform V1beta1Examples Example Gcs Source Data Format - The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
- gcs
Source GoogleCloud Aiplatform V1beta1Gcs Source - The Cloud Storage location for the input instances.
- data
Format GoogleCloud Aiplatform V1beta1Examples Example Gcs Source Data Format - The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
- gcs
Source GoogleCloud Aiplatform V1beta1Gcs Source - The Cloud Storage location for the input instances.
- data_
format GoogleCloud Aiplatform V1beta1Examples Example Gcs Source Data Format - The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
- gcs_
source GoogleCloud Aiplatform V1beta1Gcs Source - The Cloud Storage location for the input instances.
- data
Format "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.
- gcs
Source Property Map - The Cloud Storage location for the input instances.
GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormat, GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceDataFormatArgs
- Data
Format Unspecified - DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
- Jsonl
- JSONLExamples are stored in JSONL files.
- Google
Cloud Aiplatform V1beta1Examples Example Gcs Source Data Format Data Format Unspecified - DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
- Google
Cloud Aiplatform V1beta1Examples Example Gcs Source Data Format 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.
- DATA_FORMAT_UNSPECIFIED
- DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
- JSONL
- JSONLExamples are stored in JSONL files.
- "DATA_FORMAT_UNSPECIFIED"
- DATA_FORMAT_UNSPECIFIEDFormat unspecified, used when unset.
- "JSONL"
- JSONLExamples are stored in JSONL files.
GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponse, GoogleCloudAiplatformV1beta1ExamplesExampleGcsSourceResponseArgs
- Data
Format string - The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
- Gcs
Source Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Gcs Source Response - The Cloud Storage location for the input instances.
- Data
Format string - The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
- Gcs
Source GoogleCloud Aiplatform V1beta1Gcs Source Response - The Cloud Storage location for the input instances.
- data
Format String - The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
- gcs
Source GoogleCloud Aiplatform V1beta1Gcs Source Response - The Cloud Storage location for the input instances.
- data
Format string - The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
- gcs
Source GoogleCloud Aiplatform V1beta1Gcs Source Response - 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 GoogleCloud Aiplatform V1beta1Gcs Source Response - The Cloud Storage location for the input instances.
- data
Format String - The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
- gcs
Source Property Map - The Cloud Storage location for the input instances.
GoogleCloudAiplatformV1beta1ExamplesResponse, GoogleCloudAiplatformV1beta1ExamplesResponseArgs
- Example
Gcs Pulumi.Source Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Examples Example Gcs Source Response - The Cloud Storage input instances.
- Gcs
Source Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Gcs Source Response - The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
- Nearest
Neighbor objectSearch Config - 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
Pulumi.
Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Presets Response - Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
- Example
Gcs GoogleSource Cloud Aiplatform V1beta1Examples Example Gcs Source Response - The Cloud Storage input instances.
- Gcs
Source GoogleCloud Aiplatform V1beta1Gcs Source Response - The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
- Nearest
Neighbor interface{}Search Config - 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
Google
Cloud Aiplatform V1beta1Presets Response - Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
- example
Gcs GoogleSource Cloud Aiplatform V1beta1Examples Example Gcs Source Response - The Cloud Storage input instances.
- gcs
Source GoogleCloud Aiplatform V1beta1Gcs Source Response - The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
- nearest
Neighbor ObjectSearch Config - The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
- neighbor
Count Integer - The number of neighbors to return when querying for examples.
- presets
Google
Cloud Aiplatform V1beta1Presets Response - Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
- example
Gcs GoogleSource Cloud Aiplatform V1beta1Examples Example Gcs Source Response - The Cloud Storage input instances.
- gcs
Source GoogleCloud Aiplatform V1beta1Gcs Source Response - The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
- nearest
Neighbor anySearch Config - The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
- neighbor
Count number - The number of neighbors to return when querying for examples.
- presets
Google
Cloud Aiplatform V1beta1Presets Response - Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
- example_
gcs_ Googlesource Cloud Aiplatform V1beta1Examples Example Gcs Source Response - The Cloud Storage input instances.
- gcs_
source GoogleCloud Aiplatform V1beta1Gcs Source Response - The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
- nearest_
neighbor_ Anysearch_ config - 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
Google
Cloud Aiplatform V1beta1Presets Response - Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
- example
Gcs Property MapSource - The Cloud Storage input instances.
- gcs
Source Property Map - The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
- nearest
Neighbor AnySearch Config - The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
- neighbor
Count Number - The number of neighbors to return when querying for examples.
- presets Property Map
- Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.
GoogleCloudAiplatformV1beta1ExplanationMetadata, GoogleCloudAiplatformV1beta1ExplanationMetadataArgs
- Inputs Dictionary<string, string>
- Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Vertex AI. For Vertex AI-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
- Outputs Dictionary<string, string>
- Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
- Feature
Attributions stringSchema Uri - 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 stringSource - 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.
- Feature
Attributions stringSchema Uri - 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 stringSource - 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.
- feature
Attributions StringSchema Uri - 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 StringSource - 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.
- feature
Attributions stringSchema Uri - 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 stringSource - 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_ strschema_ uri - 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_ strsource - 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.
- feature
Attributions StringSchema Uri - 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 StringSource - Name of the source to generate embeddings for example based explanations.
GoogleCloudAiplatformV1beta1ExplanationMetadataResponse, GoogleCloudAiplatformV1beta1ExplanationMetadataResponseArgs
- Feature
Attributions stringSchema Uri - 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.
- Latent
Space stringSource - 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.
- Feature
Attributions stringSchema Uri - 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.
- Latent
Space stringSource - 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.
- feature
Attributions StringSchema Uri - 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.
- latent
Space StringSource - 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.
- feature
Attributions stringSchema Uri - 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.
- latent
Space stringSource - 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_ strschema_ uri - 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_ strsource - 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.
- feature
Attributions StringSchema Uri - 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.
- latent
Space StringSource - Name of the source to generate embeddings for example based explanations.
- outputs Map<String>
- Map from output names to output metadata. For Vertex AI-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
GoogleCloudAiplatformV1beta1ExplanationParameters, GoogleCloudAiplatformV1beta1ExplanationParametersArgs
- Examples
Pulumi.
Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Examples - Example-based explanations that returns the nearest neighbors from the provided dataset.
- Integrated
Gradients Pulumi.Attribution Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Integrated Gradients Attribution - 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 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).
- Sampled
Shapley Pulumi.Attribution Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Sampled Shapley Attribution - 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 Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Xrai Attribution - 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
Google
Cloud Aiplatform V1beta1Examples - Example-based explanations that returns the nearest neighbors from the provided dataset.
- Integrated
Gradients GoogleAttribution Cloud Aiplatform V1beta1Integrated Gradients Attribution - 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 []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).
- Sampled
Shapley GoogleAttribution Cloud Aiplatform V1beta1Sampled Shapley Attribution - 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 GoogleCloud Aiplatform V1beta1Xrai Attribution - 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
Google
Cloud Aiplatform V1beta1Examples - Example-based explanations that returns the nearest neighbors from the provided dataset.
- integrated
Gradients GoogleAttribution Cloud Aiplatform V1beta1Integrated Gradients Attribution - 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 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).
- sampled
Shapley GoogleAttribution Cloud Aiplatform V1beta1Sampled Shapley Attribution - 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 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.
- xrai
Attribution GoogleCloud Aiplatform V1beta1Xrai Attribution - 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
Google
Cloud Aiplatform V1beta1Examples - Example-based explanations that returns the nearest neighbors from the provided dataset.
- integrated
Gradients GoogleAttribution Cloud Aiplatform V1beta1Integrated Gradients Attribution - 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 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 GoogleAttribution Cloud Aiplatform V1beta1Sampled Shapley Attribution - 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 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.
- xrai
Attribution GoogleCloud Aiplatform V1beta1Xrai Attribution - 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
Google
Cloud Aiplatform V1beta1Examples - Example-based explanations that returns the nearest neighbors from the provided dataset.
- integrated_
gradients_ Googleattribution Cloud Aiplatform V1beta1Integrated Gradients Attribution - 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_ Googleattribution Cloud Aiplatform V1beta1Sampled Shapley Attribution - 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 GoogleCloud Aiplatform V1beta1Xrai Attribution - 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.
- integrated
Gradients Property MapAttribution - 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 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).
- sampled
Shapley Property MapAttribution - 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 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.
- xrai
Attribution Property Map - An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
GoogleCloudAiplatformV1beta1ExplanationParametersResponse, GoogleCloudAiplatformV1beta1ExplanationParametersResponseArgs
- Examples
Pulumi.
Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Examples Response - Example-based explanations that returns the nearest neighbors from the provided dataset.
- Integrated
Gradients Pulumi.Attribution Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Integrated Gradients Attribution Response - 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 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).
- Sampled
Shapley Pulumi.Attribution Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Sampled Shapley Attribution Response - 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 Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Xrai Attribution Response - 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
Google
Cloud Aiplatform V1beta1Examples Response - Example-based explanations that returns the nearest neighbors from the provided dataset.
- Integrated
Gradients GoogleAttribution Cloud Aiplatform V1beta1Integrated Gradients Attribution Response - 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 []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).
- Sampled
Shapley GoogleAttribution Cloud Aiplatform V1beta1Sampled Shapley Attribution Response - 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 GoogleCloud Aiplatform V1beta1Xrai Attribution Response - 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
Google
Cloud Aiplatform V1beta1Examples Response - Example-based explanations that returns the nearest neighbors from the provided dataset.
- integrated
Gradients GoogleAttribution Cloud Aiplatform V1beta1Integrated Gradients Attribution Response - 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 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).
- sampled
Shapley GoogleAttribution Cloud Aiplatform V1beta1Sampled Shapley Attribution Response - 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 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.
- xrai
Attribution GoogleCloud Aiplatform V1beta1Xrai Attribution Response - 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
Google
Cloud Aiplatform V1beta1Examples Response - Example-based explanations that returns the nearest neighbors from the provided dataset.
- integrated
Gradients GoogleAttribution Cloud Aiplatform V1beta1Integrated Gradients Attribution Response - 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 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 GoogleAttribution Cloud Aiplatform V1beta1Sampled Shapley Attribution Response - 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 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.
- xrai
Attribution GoogleCloud Aiplatform V1beta1Xrai Attribution Response - 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
Google
Cloud Aiplatform V1beta1Examples Response - Example-based explanations that returns the nearest neighbors from the provided dataset.
- integrated_
gradients_ Googleattribution Cloud Aiplatform V1beta1Integrated Gradients Attribution Response - 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_ Googleattribution Cloud Aiplatform V1beta1Sampled Shapley Attribution Response - 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 GoogleCloud Aiplatform V1beta1Xrai Attribution Response - 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.
- integrated
Gradients Property MapAttribution - 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 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).
- sampled
Shapley Property MapAttribution - 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 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.
- xrai
Attribution Property Map - An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
GoogleCloudAiplatformV1beta1ExplanationSpec, GoogleCloudAiplatformV1beta1ExplanationSpecArgs
- Parameters
Pulumi.
Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Explanation Parameters - Parameters that configure explaining of the Model's predictions.
- Metadata
Pulumi.
Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Explanation Metadata - Optional. Metadata describing the Model's input and output for explanation.
- Parameters
Google
Cloud Aiplatform V1beta1Explanation Parameters - Parameters that configure explaining of the Model's predictions.
- Metadata
Google
Cloud Aiplatform V1beta1Explanation Metadata - Optional. Metadata describing the Model's input and output for explanation.
- parameters
Google
Cloud Aiplatform V1beta1Explanation Parameters - Parameters that configure explaining of the Model's predictions.
- metadata
Google
Cloud Aiplatform V1beta1Explanation Metadata - Optional. Metadata describing the Model's input and output for explanation.
- parameters
Google
Cloud Aiplatform V1beta1Explanation Parameters - Parameters that configure explaining of the Model's predictions.
- metadata
Google
Cloud Aiplatform V1beta1Explanation Metadata - Optional. Metadata describing the Model's input and output for explanation.
- parameters
Google
Cloud Aiplatform V1beta1Explanation Parameters - Parameters that configure explaining of the Model's predictions.
- metadata
Google
Cloud Aiplatform V1beta1Explanation Metadata - Optional. Metadata describing the Model's input and output for explanation.
- parameters Property Map
- Parameters that configure explaining of the Model's predictions.
- metadata Property Map
- Optional. Metadata describing the Model's input and output for explanation.
GoogleCloudAiplatformV1beta1ExplanationSpecResponse, GoogleCloudAiplatformV1beta1ExplanationSpecResponseArgs
- Metadata
Pulumi.
Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Explanation Metadata Response - Optional. Metadata describing the Model's input and output for explanation.
- Parameters
Pulumi.
Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Explanation Parameters Response - Parameters that configure explaining of the Model's predictions.
- Metadata
Google
Cloud Aiplatform V1beta1Explanation Metadata Response - Optional. Metadata describing the Model's input and output for explanation.
- Parameters
Google
Cloud Aiplatform V1beta1Explanation Parameters Response - Parameters that configure explaining of the Model's predictions.
- metadata
Google
Cloud Aiplatform V1beta1Explanation Metadata Response - Optional. Metadata describing the Model's input and output for explanation.
- parameters
Google
Cloud Aiplatform V1beta1Explanation Parameters Response - Parameters that configure explaining of the Model's predictions.
- metadata
Google
Cloud Aiplatform V1beta1Explanation Metadata Response - Optional. Metadata describing the Model's input and output for explanation.
- parameters
Google
Cloud Aiplatform V1beta1Explanation Parameters Response - Parameters that configure explaining of the Model's predictions.
- metadata
Google
Cloud Aiplatform V1beta1Explanation Metadata Response - Optional. Metadata describing the Model's input and output for explanation.
- parameters
Google
Cloud Aiplatform V1beta1Explanation Parameters Response - Parameters that configure explaining of the Model's predictions.
- metadata Property Map
- Optional. Metadata describing the Model's input and output for explanation.
- parameters Property Map
- Parameters that configure explaining of the Model's predictions.
GoogleCloudAiplatformV1beta1FeatureNoiseSigma, GoogleCloudAiplatformV1beta1FeatureNoiseSigmaArgs
- Noise
Sigma List<Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Feature Noise Sigma Noise Sigma For Feature> - Noise sigma per feature. No noise is added to features that are not set.
- Noise
Sigma []GoogleCloud Aiplatform V1beta1Feature Noise Sigma Noise Sigma For Feature - Noise sigma per feature. No noise is added to features that are not set.
- noise
Sigma List<GoogleCloud Aiplatform V1beta1Feature Noise Sigma Noise Sigma For Feature> - Noise sigma per feature. No noise is added to features that are not set.
- noise
Sigma GoogleCloud Aiplatform V1beta1Feature Noise Sigma Noise Sigma For Feature[] - Noise sigma per feature. No noise is added to features that are not set.
- noise_
sigma Sequence[GoogleCloud Aiplatform V1beta1Feature Noise Sigma Noise Sigma For Feature] - Noise sigma per feature. No noise is added to features that are not set.
- noise
Sigma List<Property Map> - Noise sigma per feature. No noise is added to features that are not set.
GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeature, GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureArgs
- Name string
- The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
- Sigma double
- This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
- Name string
- The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
- Sigma float64
- This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
- name String
- The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
- sigma Double
- This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
- name string
- The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
- sigma number
- This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
- name str
- The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
- sigma float
- This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
- name String
- The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
- sigma Number
- This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponse, GoogleCloudAiplatformV1beta1FeatureNoiseSigmaNoiseSigmaForFeatureResponseArgs
- Name string
- The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
- Sigma double
- This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
- Name string
- The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
- Sigma float64
- This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
- name String
- The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
- sigma Double
- This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
- name string
- The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
- sigma number
- This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
- name str
- The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
- sigma float
- This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
- name String
- The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
- sigma Number
- This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.
GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponse, GoogleCloudAiplatformV1beta1FeatureNoiseSigmaResponseArgs
- Noise
Sigma List<Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Feature Noise Sigma Noise Sigma For Feature Response> - Noise sigma per feature. No noise is added to features that are not set.
- Noise
Sigma []GoogleCloud Aiplatform V1beta1Feature Noise Sigma Noise Sigma For Feature Response - Noise sigma per feature. No noise is added to features that are not set.
- noise
Sigma List<GoogleCloud Aiplatform V1beta1Feature Noise Sigma Noise Sigma For Feature Response> - Noise sigma per feature. No noise is added to features that are not set.
- noise
Sigma GoogleCloud Aiplatform V1beta1Feature Noise Sigma Noise Sigma For Feature Response[] - Noise sigma per feature. No noise is added to features that are not set.
- noise_
sigma Sequence[GoogleCloud Aiplatform V1beta1Feature Noise Sigma Noise Sigma For Feature Response] - Noise sigma per feature. No noise is added to features that are not set.
- noise
Sigma List<Property Map> - Noise sigma per feature. No noise is added to features that are not set.
GoogleCloudAiplatformV1beta1FilterSplit, GoogleCloudAiplatformV1beta1FilterSplitArgs
- Test
Filter 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.
- Training
Filter 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.
- Validation
Filter 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 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.
- Training
Filter 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.
- Validation
Filter 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 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.
- training
Filter 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.
- validation
Filter 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 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.
- training
Filter 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.
- validation
Filter 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.
- test
Filter 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.
- training
Filter 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.
- validation
Filter String - A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
GoogleCloudAiplatformV1beta1FilterSplitResponse, GoogleCloudAiplatformV1beta1FilterSplitResponseArgs
- Test
Filter 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.
- Training
Filter 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.
- Validation
Filter 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 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.
- Training
Filter 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.
- Validation
Filter 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 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.
- training
Filter 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.
- validation
Filter 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 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.
- training
Filter 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.
- validation
Filter 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.
- test
Filter 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.
- training
Filter 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.
- validation
Filter String - A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
GoogleCloudAiplatformV1beta1FractionSplit, GoogleCloudAiplatformV1beta1FractionSplitArgs
- Test
Fraction double - The fraction of the input data that is to be used to evaluate the Model.
- Training
Fraction double - The fraction of the input data that is to be used to train the Model.
- Validation
Fraction double - The fraction of the input data that is to be used to validate the Model.
- Test
Fraction float64 - The fraction of the input data that is to be used to evaluate the Model.
- Training
Fraction float64 - The fraction of the input data that is to be used to train the Model.
- Validation
Fraction float64 - The fraction of the input data that is to be used to validate the Model.
- test
Fraction Double - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction Double - The fraction of the input data that is to be used to train the Model.
- validation
Fraction Double - The fraction of the input data that is to be used to validate the Model.
- test
Fraction number - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction number - The fraction of the input data that is to be used to train the Model.
- validation
Fraction 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.
- test
Fraction Number - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction Number - The fraction of the input data that is to be used to train the Model.
- validation
Fraction Number - The fraction of the input data that is to be used to validate the Model.
GoogleCloudAiplatformV1beta1FractionSplitResponse, GoogleCloudAiplatformV1beta1FractionSplitResponseArgs
- Test
Fraction double - The fraction of the input data that is to be used to evaluate the Model.
- Training
Fraction double - The fraction of the input data that is to be used to train the Model.
- Validation
Fraction double - The fraction of the input data that is to be used to validate the Model.
- Test
Fraction float64 - The fraction of the input data that is to be used to evaluate the Model.
- Training
Fraction float64 - The fraction of the input data that is to be used to train the Model.
- Validation
Fraction float64 - The fraction of the input data that is to be used to validate the Model.
- test
Fraction Double - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction Double - The fraction of the input data that is to be used to train the Model.
- validation
Fraction Double - The fraction of the input data that is to be used to validate the Model.
- test
Fraction number - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction number - The fraction of the input data that is to be used to train the Model.
- validation
Fraction 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.
- test
Fraction Number - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction Number - The fraction of the input data that is to be used to train the Model.
- validation
Fraction Number - The fraction of the input data that is to be used to validate the Model.
GoogleCloudAiplatformV1beta1GcsDestination, GoogleCloudAiplatformV1beta1GcsDestinationArgs
- Output
Uri stringPrefix - 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 stringPrefix - 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 StringPrefix - 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 stringPrefix - 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_ strprefix - 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 StringPrefix - Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
GoogleCloudAiplatformV1beta1GcsDestinationResponse, GoogleCloudAiplatformV1beta1GcsDestinationResponseArgs
- Output
Uri stringPrefix - 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 stringPrefix - 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 StringPrefix - 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 stringPrefix - 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_ strprefix - 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 StringPrefix - Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.
GoogleCloudAiplatformV1beta1GcsSource, GoogleCloudAiplatformV1beta1GcsSourceArgs
- Uris List<string>
- Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
- Uris []string
- Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
- uris List<String>
- Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
- uris string[]
- Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
- uris Sequence[str]
- Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
- uris List<String>
- Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
GoogleCloudAiplatformV1beta1GcsSourceResponse, GoogleCloudAiplatformV1beta1GcsSourceResponseArgs
- Uris List<string>
- Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
- Uris []string
- Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
- uris List<String>
- Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
- uris string[]
- Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
- uris Sequence[str]
- Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
- uris List<String>
- Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames.
GoogleCloudAiplatformV1beta1InputDataConfig, GoogleCloudAiplatformV1beta1InputDataConfigArgs
- Dataset
Id 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.
- Annotation
Schema stringUri - 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 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.
- Bigquery
Destination Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Big Query Destination - 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,validationandtest. * 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 Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Filter Split - Split based on the provided filters for each set.
- Fraction
Split Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Fraction Split - Split based on fractions defining the size of each set.
- Gcs
Destination Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Gcs Destination - 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 boolUse Assignment - Whether to persist the ML use assignment to data item system labels.
- Predefined
Split Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Predefined Split - Supported only for tabular Datasets. Split based on a predefined key.
- Saved
Query stringId - 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 Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Stratified Split - Supported only for tabular Datasets. Split based on the distribution of the specified column.
- Timestamp
Split Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Timestamp Split - Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
- Dataset
Id 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.
- Annotation
Schema stringUri - 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 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.
- Bigquery
Destination GoogleCloud Aiplatform V1beta1Big Query Destination - 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,validationandtest. * 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 GoogleCloud Aiplatform V1beta1Filter Split - Split based on the provided filters for each set.
- Fraction
Split GoogleCloud Aiplatform V1beta1Fraction Split - Split based on fractions defining the size of each set.
- Gcs
Destination GoogleCloud Aiplatform V1beta1Gcs Destination - 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 boolUse Assignment - Whether to persist the ML use assignment to data item system labels.
- Predefined
Split GoogleCloud Aiplatform V1beta1Predefined Split - Supported only for tabular Datasets. Split based on a predefined key.
- Saved
Query stringId - 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 GoogleCloud Aiplatform V1beta1Stratified Split - Supported only for tabular Datasets. Split based on the distribution of the specified column.
- Timestamp
Split GoogleCloud Aiplatform V1beta1Timestamp Split - Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
- dataset
Id 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.
- annotation
Schema StringUri - 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 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.
- bigquery
Destination GoogleCloud Aiplatform V1beta1Big Query Destination - 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,validationandtest. * 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 GoogleCloud Aiplatform V1beta1Filter Split - Split based on the provided filters for each set.
- fraction
Split GoogleCloud Aiplatform V1beta1Fraction Split - Split based on fractions defining the size of each set.
- gcs
Destination GoogleCloud Aiplatform V1beta1Gcs Destination - 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 BooleanUse Assignment - Whether to persist the ML use assignment to data item system labels.
- predefined
Split GoogleCloud Aiplatform V1beta1Predefined Split - Supported only for tabular Datasets. Split based on a predefined key.
- saved
Query StringId - 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 GoogleCloud Aiplatform V1beta1Stratified Split - Supported only for tabular Datasets. Split based on the distribution of the specified column.
- timestamp
Split GoogleCloud Aiplatform V1beta1Timestamp Split - Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
- dataset
Id 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.
- annotation
Schema stringUri - 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 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.
- bigquery
Destination GoogleCloud Aiplatform V1beta1Big Query Destination - 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,validationandtest. * 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 GoogleCloud Aiplatform V1beta1Filter Split - Split based on the provided filters for each set.
- fraction
Split GoogleCloud Aiplatform V1beta1Fraction Split - Split based on fractions defining the size of each set.
- gcs
Destination GoogleCloud Aiplatform V1beta1Gcs Destination - 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 booleanUse Assignment - Whether to persist the ML use assignment to data item system labels.
- predefined
Split GoogleCloud Aiplatform V1beta1Predefined Split - Supported only for tabular Datasets. Split based on a predefined key.
- saved
Query stringId - 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 GoogleCloud Aiplatform V1beta1Stratified Split - Supported only for tabular Datasets. Split based on the distribution of the specified column.
- timestamp
Split GoogleCloud Aiplatform V1beta1Timestamp Split - 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_ struri - 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 GoogleCloud Aiplatform V1beta1Big Query Destination - 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,validationandtest. * 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 GoogleCloud Aiplatform V1beta1Filter Split - Split based on the provided filters for each set.
- fraction_
split GoogleCloud Aiplatform V1beta1Fraction Split - Split based on fractions defining the size of each set.
- gcs_
destination GoogleCloud Aiplatform V1beta1Gcs Destination - 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_ booluse_ assignment - Whether to persist the ML use assignment to data item system labels.
- predefined_
split GoogleCloud Aiplatform V1beta1Predefined Split - Supported only for tabular Datasets. Split based on a predefined key.
- saved_
query_ strid - 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 GoogleCloud Aiplatform V1beta1Stratified Split - Supported only for tabular Datasets. Split based on the distribution of the specified column.
- timestamp_
split GoogleCloud Aiplatform V1beta1Timestamp Split - Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
- dataset
Id 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.
- annotation
Schema StringUri - 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 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.
- bigquery
Destination 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,validationandtest. * 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 Property Map - Split based on the provided filters for each set.
- fraction
Split Property Map - Split based on fractions defining the size of each set.
- gcs
Destination 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}" - persist
Ml BooleanUse Assignment - Whether to persist the ML use assignment to data item system labels.
- predefined
Split Property Map - Supported only for tabular Datasets. Split based on a predefined key.
- saved
Query StringId - 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 Property Map - Supported only for tabular Datasets. Split based on the distribution of the specified column.
- timestamp
Split Property Map - Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
GoogleCloudAiplatformV1beta1InputDataConfigResponse, GoogleCloudAiplatformV1beta1InputDataConfigResponseArgs
- Annotation
Schema stringUri - 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 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.
- Bigquery
Destination Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Big Query Destination Response - 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,validationandtest. * 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 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.
- Filter
Split Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Filter Split Response - Split based on the provided filters for each set.
- Fraction
Split Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Fraction Split Response - Split based on fractions defining the size of each set.
- Gcs
Destination Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Gcs Destination Response - 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 boolUse Assignment - Whether to persist the ML use assignment to data item system labels.
- Predefined
Split Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Predefined Split Response - Supported only for tabular Datasets. Split based on a predefined key.
- Saved
Query stringId - 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 Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Stratified Split Response - Supported only for tabular Datasets. Split based on the distribution of the specified column.
- Timestamp
Split Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Timestamp Split Response - Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
- Annotation
Schema stringUri - 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 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.
- Bigquery
Destination GoogleCloud Aiplatform V1beta1Big Query Destination Response - 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,validationandtest. * 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 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.
- Filter
Split GoogleCloud Aiplatform V1beta1Filter Split Response - Split based on the provided filters for each set.
- Fraction
Split GoogleCloud Aiplatform V1beta1Fraction Split Response - Split based on fractions defining the size of each set.
- Gcs
Destination GoogleCloud Aiplatform V1beta1Gcs Destination Response - 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 boolUse Assignment - Whether to persist the ML use assignment to data item system labels.
- Predefined
Split GoogleCloud Aiplatform V1beta1Predefined Split Response - Supported only for tabular Datasets. Split based on a predefined key.
- Saved
Query stringId - 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 GoogleCloud Aiplatform V1beta1Stratified Split Response - Supported only for tabular Datasets. Split based on the distribution of the specified column.
- Timestamp
Split GoogleCloud Aiplatform V1beta1Timestamp Split Response - Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
- annotation
Schema StringUri - 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 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.
- bigquery
Destination GoogleCloud Aiplatform V1beta1Big Query Destination Response - 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,validationandtest. * 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 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.
- filter
Split GoogleCloud Aiplatform V1beta1Filter Split Response - Split based on the provided filters for each set.
- fraction
Split GoogleCloud Aiplatform V1beta1Fraction Split Response - Split based on fractions defining the size of each set.
- gcs
Destination GoogleCloud Aiplatform V1beta1Gcs Destination Response - 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 BooleanUse Assignment - Whether to persist the ML use assignment to data item system labels.
- predefined
Split GoogleCloud Aiplatform V1beta1Predefined Split Response - Supported only for tabular Datasets. Split based on a predefined key.
- saved
Query StringId - 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 GoogleCloud Aiplatform V1beta1Stratified Split Response - Supported only for tabular Datasets. Split based on the distribution of the specified column.
- timestamp
Split GoogleCloud Aiplatform V1beta1Timestamp Split Response - Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
- annotation
Schema stringUri - 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 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.
- bigquery
Destination GoogleCloud Aiplatform V1beta1Big Query Destination Response - 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,validationandtest. * 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 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.
- filter
Split GoogleCloud Aiplatform V1beta1Filter Split Response - Split based on the provided filters for each set.
- fraction
Split GoogleCloud Aiplatform V1beta1Fraction Split Response - Split based on fractions defining the size of each set.
- gcs
Destination GoogleCloud Aiplatform V1beta1Gcs Destination Response - 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 booleanUse Assignment - Whether to persist the ML use assignment to data item system labels.
- predefined
Split GoogleCloud Aiplatform V1beta1Predefined Split Response - Supported only for tabular Datasets. Split based on a predefined key.
- saved
Query stringId - 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 GoogleCloud Aiplatform V1beta1Stratified Split Response - Supported only for tabular Datasets. Split based on the distribution of the specified column.
- timestamp
Split GoogleCloud Aiplatform V1beta1Timestamp Split Response - Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
- annotation_
schema_ struri - 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 GoogleCloud Aiplatform V1beta1Big Query Destination Response - 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,validationandtest. * 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 GoogleCloud Aiplatform V1beta1Filter Split Response - Split based on the provided filters for each set.
- fraction_
split GoogleCloud Aiplatform V1beta1Fraction Split Response - Split based on fractions defining the size of each set.
- gcs_
destination GoogleCloud Aiplatform V1beta1Gcs Destination Response - 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_ booluse_ assignment - Whether to persist the ML use assignment to data item system labels.
- predefined_
split GoogleCloud Aiplatform V1beta1Predefined Split Response - Supported only for tabular Datasets. Split based on a predefined key.
- saved_
query_ strid - 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 GoogleCloud Aiplatform V1beta1Stratified Split Response - Supported only for tabular Datasets. Split based on the distribution of the specified column.
- timestamp_
split GoogleCloud Aiplatform V1beta1Timestamp Split Response - Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
- annotation
Schema StringUri - 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 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.
- bigquery
Destination 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,validationandtest. * 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 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.
- filter
Split Property Map - Split based on the provided filters for each set.
- fraction
Split Property Map - Split based on fractions defining the size of each set.
- gcs
Destination 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}" - persist
Ml BooleanUse Assignment - Whether to persist the ML use assignment to data item system labels.
- predefined
Split Property Map - Supported only for tabular Datasets. Split based on a predefined key.
- saved
Query StringId - 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 Property Map - Supported only for tabular Datasets. Split based on the distribution of the specified column.
- timestamp
Split Property Map - Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
GoogleCloudAiplatformV1beta1IntegratedGradientsAttribution, GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionArgs
- 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 Pulumi.Config Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Blur Baseline Config - 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 Pulumi.Config Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Smooth Grad Config - 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 GoogleConfig Cloud Aiplatform V1beta1Blur Baseline Config - 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 GoogleConfig Cloud Aiplatform V1beta1Smooth Grad Config - 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 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.
- blur
Baseline GoogleConfig Cloud Aiplatform V1beta1Blur Baseline Config - 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 GoogleConfig Cloud Aiplatform V1beta1Smooth Grad Config - 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 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 GoogleConfig Cloud Aiplatform V1beta1Blur Baseline Config - 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 GoogleConfig Cloud Aiplatform V1beta1Smooth Grad Config - 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_ Googleconfig Cloud Aiplatform V1beta1Blur Baseline Config - 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_ Googleconfig Cloud Aiplatform V1beta1Smooth Grad Config - 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 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 Property MapConfig - 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 Property MapConfig - Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponse, GoogleCloudAiplatformV1beta1IntegratedGradientsAttributionResponseArgs
- Blur
Baseline Pulumi.Config Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Blur Baseline Config Response - 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 Pulumi.Config Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Smooth Grad Config Response - 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 GoogleConfig Cloud Aiplatform V1beta1Blur Baseline Config Response - 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 GoogleConfig Cloud Aiplatform V1beta1Smooth Grad Config Response - 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 GoogleConfig Cloud Aiplatform V1beta1Blur Baseline Config Response - 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 GoogleConfig Cloud Aiplatform V1beta1Smooth Grad Config Response - 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 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.
- blur
Baseline GoogleConfig Cloud Aiplatform V1beta1Blur Baseline Config Response - 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 GoogleConfig Cloud Aiplatform V1beta1Smooth Grad Config Response - 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 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_ Googleconfig Cloud Aiplatform V1beta1Blur Baseline Config Response - 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_ Googleconfig Cloud Aiplatform V1beta1Smooth Grad Config Response - 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 Property MapConfig - 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 Property MapConfig - 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 Number - The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
GoogleCloudAiplatformV1beta1Model, GoogleCloudAiplatformV1beta1ModelArgs
- Display
Name string - 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 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.
- Container
Spec Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Model Container Spec - 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.
- Encryption
Spec Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Encryption Spec - 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.
- Explanation
Spec Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Explanation Spec - 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.
- Metadata
Schema stringUri - 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.
- Predict
Schemata Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Predict Schemata - The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
- Version
Aliases 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. - Version
Description string - The description of this version.
- Display
Name string - 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 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.
- Container
Spec GoogleCloud Aiplatform V1beta1Model Container Spec - 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.
- Encryption
Spec GoogleCloud Aiplatform V1beta1Encryption Spec - 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.
- Explanation
Spec GoogleCloud Aiplatform V1beta1Explanation Spec - 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.
- Metadata
Schema stringUri - 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.
- Predict
Schemata GoogleCloud Aiplatform V1beta1Predict Schemata - The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
- Version
Aliases []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. - Version
Description string - The description of this version.
- display
Name String - 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 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.
- container
Spec GoogleCloud Aiplatform V1beta1Model Container Spec - 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.
- encryption
Spec GoogleCloud Aiplatform V1beta1Encryption Spec - 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.
- explanation
Spec GoogleCloud Aiplatform V1beta1Explanation Spec - 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.
- metadata
Schema StringUri - 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.
- predict
Schemata GoogleCloud Aiplatform V1beta1Predict Schemata - The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
- version
Aliases 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. - version
Description String - The description of this version.
- display
Name string - 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 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.
- container
Spec GoogleCloud Aiplatform V1beta1Model Container Spec - 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.
- encryption
Spec GoogleCloud Aiplatform V1beta1Encryption Spec - 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.
- explanation
Spec GoogleCloud Aiplatform V1beta1Explanation Spec - 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.
- metadata
Schema stringUri - 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.
- predict
Schemata GoogleCloud Aiplatform V1beta1Predict Schemata - The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
- version
Aliases 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. - version
Description 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 GoogleCloud Aiplatform V1beta1Model Container Spec - 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 GoogleCloud Aiplatform V1beta1Encryption Spec - 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 GoogleCloud Aiplatform V1beta1Explanation Spec - 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_ struri - Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
- name str
- The resource name of the Model.
- predict_
schemata GoogleCloud Aiplatform V1beta1Predict Schemata - 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.
- display
Name String - 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 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.
- container
Spec 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.
- encryption
Spec 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.
- explanation
Spec 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.
- metadata
Schema StringUri - 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.
- predict
Schemata Property Map - The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
- version
Aliases 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. - version
Description String - The description of this version.
GoogleCloudAiplatformV1beta1ModelContainerSpec, GoogleCloudAiplatformV1beta1ModelContainerSpecArgs
- Image
Uri 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 DockerCMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from thecommandfield runs without any additional arguments. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. If you don't specify this field and don't specify thecommandfield, then the container'sENTRYPOINTandCMDdetermine what runs based on their default behavior. See the Docker documentation about howCMDandENTRYPOINTinteract. 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 theargsfield 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'sENTRYPOINTruns, in conjunction with the args field or the container'sCMD, if either exists. If this field is not specified and the container does not have anENTRYPOINT, then refer to the Docker documentation about howCMDandENTRYPOINTinteract. If you specify this field, then you can also specify theargsfield to provide additional arguments for this command. However, if you specify this field, then the container'sCMDis ignored. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. 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 thecommandfield of the Kubernetes Containers v1 core API. - Deployment
Timeout string - Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
- Env
List<Pulumi.
Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Env Var> - 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_2to have the valuefoo 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 theenvfield of the Kubernetes Containers v1 core API. - Health
Probe Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Probe - Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
- Health
Route 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/barpath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - Ports
List<Pulumi.
Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Port> - 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 theportsfield of the Kubernetes Containers v1 core API. - Predict
Route 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/foopath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - 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.
- Startup
Probe Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Probe - Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
- Image
Uri 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 DockerCMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from thecommandfield runs without any additional arguments. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. If you don't specify this field and don't specify thecommandfield, then the container'sENTRYPOINTandCMDdetermine what runs based on their default behavior. See the Docker documentation about howCMDandENTRYPOINTinteract. 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 theargsfield 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'sENTRYPOINTruns, in conjunction with the args field or the container'sCMD, if either exists. If this field is not specified and the container does not have anENTRYPOINT, then refer to the Docker documentation about howCMDandENTRYPOINTinteract. If you specify this field, then you can also specify theargsfield to provide additional arguments for this command. However, if you specify this field, then the container'sCMDis ignored. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. 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 thecommandfield of the Kubernetes Containers v1 core API. - Deployment
Timeout string - Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
- Env
[]Google
Cloud Aiplatform V1beta1Env Var - 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_2to have the valuefoo 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 theenvfield of the Kubernetes Containers v1 core API. - Health
Probe GoogleCloud Aiplatform V1beta1Probe - Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
- Health
Route 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/barpath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - Ports
[]Google
Cloud Aiplatform V1beta1Port - 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 theportsfield of the Kubernetes Containers v1 core API. - Predict
Route 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/foopath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - 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.
- Startup
Probe GoogleCloud Aiplatform V1beta1Probe - Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
- image
Uri 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 DockerCMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from thecommandfield runs without any additional arguments. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. If you don't specify this field and don't specify thecommandfield, then the container'sENTRYPOINTandCMDdetermine what runs based on their default behavior. See the Docker documentation about howCMDandENTRYPOINTinteract. 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 theargsfield 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'sENTRYPOINTruns, in conjunction with the args field or the container'sCMD, if either exists. If this field is not specified and the container does not have anENTRYPOINT, then refer to the Docker documentation about howCMDandENTRYPOINTinteract. If you specify this field, then you can also specify theargsfield to provide additional arguments for this command. However, if you specify this field, then the container'sCMDis ignored. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. 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 thecommandfield of the Kubernetes Containers v1 core API. - deployment
Timeout String - Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
- env
List<Google
Cloud Aiplatform V1beta1Env Var> - 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_2to have the valuefoo 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 theenvfield of the Kubernetes Containers v1 core API. - health
Probe GoogleCloud Aiplatform V1beta1Probe - Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
- health
Route 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/barpath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - ports
List<Google
Cloud Aiplatform V1beta1Port> - 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 theportsfield of the Kubernetes Containers v1 core API. - predict
Route 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/foopath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - 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.
- startup
Probe GoogleCloud Aiplatform V1beta1Probe - Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
- image
Uri 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 DockerCMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from thecommandfield runs without any additional arguments. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. If you don't specify this field and don't specify thecommandfield, then the container'sENTRYPOINTandCMDdetermine what runs based on their default behavior. See the Docker documentation about howCMDandENTRYPOINTinteract. 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 theargsfield 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'sENTRYPOINTruns, in conjunction with the args field or the container'sCMD, if either exists. If this field is not specified and the container does not have anENTRYPOINT, then refer to the Docker documentation about howCMDandENTRYPOINTinteract. If you specify this field, then you can also specify theargsfield to provide additional arguments for this command. However, if you specify this field, then the container'sCMDis ignored. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. 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 thecommandfield of the Kubernetes Containers v1 core API. - deployment
Timeout string - Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
- env
Google
Cloud Aiplatform V1beta1Env Var[] - 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_2to have the valuefoo 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 theenvfield of the Kubernetes Containers v1 core API. - health
Probe GoogleCloud Aiplatform V1beta1Probe - Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
- health
Route 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/barpath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - ports
Google
Cloud Aiplatform V1beta1Port[] - 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 theportsfield of the Kubernetes Containers v1 core API. - predict
Route 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/foopath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - 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.
- startup
Probe GoogleCloud Aiplatform V1beta1Probe - 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 DockerCMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from thecommandfield runs without any additional arguments. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. If you don't specify this field and don't specify thecommandfield, then the container'sENTRYPOINTandCMDdetermine what runs based on their default behavior. See the Docker documentation about howCMDandENTRYPOINTinteract. 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 theargsfield 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'sENTRYPOINTruns, in conjunction with the args field or the container'sCMD, if either exists. If this field is not specified and the container does not have anENTRYPOINT, then refer to the Docker documentation about howCMDandENTRYPOINTinteract. If you specify this field, then you can also specify theargsfield to provide additional arguments for this command. However, if you specify this field, then the container'sCMDis ignored. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. 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 thecommandfield of the Kubernetes Containers v1 core API. - deployment_
timeout str - Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
- env
Sequence[Google
Cloud Aiplatform V1beta1Env Var] - 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_2to have the valuefoo 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 theenvfield of the Kubernetes Containers v1 core API. - health_
probe GoogleCloud Aiplatform V1beta1Probe - 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/barpath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - ports
Sequence[Google
Cloud Aiplatform V1beta1Port] - 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 theportsfield 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/foopath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - 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 GoogleCloud Aiplatform V1beta1Probe - Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
- image
Uri 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 DockerCMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from thecommandfield runs without any additional arguments. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. If you don't specify this field and don't specify thecommandfield, then the container'sENTRYPOINTandCMDdetermine what runs based on their default behavior. See the Docker documentation about howCMDandENTRYPOINTinteract. 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 theargsfield 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'sENTRYPOINTruns, in conjunction with the args field or the container'sCMD, if either exists. If this field is not specified and the container does not have anENTRYPOINT, then refer to the Docker documentation about howCMDandENTRYPOINTinteract. If you specify this field, then you can also specify theargsfield to provide additional arguments for this command. However, if you specify this field, then the container'sCMDis ignored. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. 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 thecommandfield of the Kubernetes Containers v1 core API. - deployment
Timeout 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_2to have the valuefoo 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 theenvfield of the Kubernetes Containers v1 core API. - health
Probe Property Map - Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
- health
Route 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/barpath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment 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 theportsfield of the Kubernetes Containers v1 core API. - predict
Route 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/foopath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - 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.
- startup
Probe Property Map - Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
GoogleCloudAiplatformV1beta1ModelContainerSpecResponse, GoogleCloudAiplatformV1beta1ModelContainerSpecResponseArgs
- Args List<string>
- Immutable. Specifies arguments for the command that runs when the container starts. This overrides the container's
CMD. Specify this field as an array of executable and arguments, similar to a DockerCMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from thecommandfield runs without any additional arguments. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. If you don't specify this field and don't specify thecommandfield, then the container'sENTRYPOINTandCMDdetermine what runs based on their default behavior. See the Docker documentation about howCMDandENTRYPOINTinteract. 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 theargsfield 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'sENTRYPOINTruns, in conjunction with the args field or the container'sCMD, if either exists. If this field is not specified and the container does not have anENTRYPOINT, then refer to the Docker documentation about howCMDandENTRYPOINTinteract. If you specify this field, then you can also specify theargsfield to provide additional arguments for this command. However, if you specify this field, then the container'sCMDis ignored. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. 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 thecommandfield of the Kubernetes Containers v1 core API. - Deployment
Timeout string - Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
- Env
List<Pulumi.
Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Env Var Response> - 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_2to have the valuefoo 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 theenvfield of the Kubernetes Containers v1 core API. - Health
Probe Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Probe Response - Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
- Health
Route 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/barpath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - Image
Uri 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.
Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Port Response> - 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 theportsfield of the Kubernetes Containers v1 core API. - Predict
Route 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/foopath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - 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.
- Startup
Probe Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Probe Response - 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 DockerCMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from thecommandfield runs without any additional arguments. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. If you don't specify this field and don't specify thecommandfield, then the container'sENTRYPOINTandCMDdetermine what runs based on their default behavior. See the Docker documentation about howCMDandENTRYPOINTinteract. 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 theargsfield 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'sENTRYPOINTruns, in conjunction with the args field or the container'sCMD, if either exists. If this field is not specified and the container does not have anENTRYPOINT, then refer to the Docker documentation about howCMDandENTRYPOINTinteract. If you specify this field, then you can also specify theargsfield to provide additional arguments for this command. However, if you specify this field, then the container'sCMDis ignored. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. 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 thecommandfield of the Kubernetes Containers v1 core API. - Deployment
Timeout string - Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
- Env
[]Google
Cloud Aiplatform V1beta1Env Var Response - 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_2to have the valuefoo 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 theenvfield of the Kubernetes Containers v1 core API. - Health
Probe GoogleCloud Aiplatform V1beta1Probe Response - Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
- Health
Route 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/barpath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - Image
Uri 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
[]Google
Cloud Aiplatform V1beta1Port Response - 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 theportsfield of the Kubernetes Containers v1 core API. - Predict
Route 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/foopath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - 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.
- Startup
Probe GoogleCloud Aiplatform V1beta1Probe Response - 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 DockerCMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from thecommandfield runs without any additional arguments. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. If you don't specify this field and don't specify thecommandfield, then the container'sENTRYPOINTandCMDdetermine what runs based on their default behavior. See the Docker documentation about howCMDandENTRYPOINTinteract. 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 theargsfield 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'sENTRYPOINTruns, in conjunction with the args field or the container'sCMD, if either exists. If this field is not specified and the container does not have anENTRYPOINT, then refer to the Docker documentation about howCMDandENTRYPOINTinteract. If you specify this field, then you can also specify theargsfield to provide additional arguments for this command. However, if you specify this field, then the container'sCMDis ignored. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. 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 thecommandfield of the Kubernetes Containers v1 core API. - deployment
Timeout String - Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
- env
List<Google
Cloud Aiplatform V1beta1Env Var Response> - 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_2to have the valuefoo 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 theenvfield of the Kubernetes Containers v1 core API. - health
Probe GoogleCloud Aiplatform V1beta1Probe Response - Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
- health
Route 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/barpath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - image
Uri 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<Google
Cloud Aiplatform V1beta1Port Response> - 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 theportsfield of the Kubernetes Containers v1 core API. - predict
Route 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/foopath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - 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.
- startup
Probe GoogleCloud Aiplatform V1beta1Probe Response - 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 DockerCMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from thecommandfield runs without any additional arguments. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. If you don't specify this field and don't specify thecommandfield, then the container'sENTRYPOINTandCMDdetermine what runs based on their default behavior. See the Docker documentation about howCMDandENTRYPOINTinteract. 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 theargsfield 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'sENTRYPOINTruns, in conjunction with the args field or the container'sCMD, if either exists. If this field is not specified and the container does not have anENTRYPOINT, then refer to the Docker documentation about howCMDandENTRYPOINTinteract. If you specify this field, then you can also specify theargsfield to provide additional arguments for this command. However, if you specify this field, then the container'sCMDis ignored. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. 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 thecommandfield of the Kubernetes Containers v1 core API. - deployment
Timeout string - Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
- env
Google
Cloud Aiplatform V1beta1Env Var Response[] - 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_2to have the valuefoo 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 theenvfield of the Kubernetes Containers v1 core API. - health
Probe GoogleCloud Aiplatform V1beta1Probe Response - Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
- health
Route 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/barpath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - image
Uri 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
Google
Cloud Aiplatform V1beta1Port Response[] - 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 theportsfield of the Kubernetes Containers v1 core API. - predict
Route 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/foopath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - 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.
- startup
Probe GoogleCloud Aiplatform V1beta1Probe Response - 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 DockerCMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from thecommandfield runs without any additional arguments. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. If you don't specify this field and don't specify thecommandfield, then the container'sENTRYPOINTandCMDdetermine what runs based on their default behavior. See the Docker documentation about howCMDandENTRYPOINTinteract. 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 theargsfield 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'sENTRYPOINTruns, in conjunction with the args field or the container'sCMD, if either exists. If this field is not specified and the container does not have anENTRYPOINT, then refer to the Docker documentation about howCMDandENTRYPOINTinteract. If you specify this field, then you can also specify theargsfield to provide additional arguments for this command. However, if you specify this field, then the container'sCMDis ignored. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. 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 thecommandfield of the Kubernetes Containers v1 core API. - deployment_
timeout str - Immutable. Deployment timeout. TODO (b/306244185): Revise documentation before exposing.
- env
Sequence[Google
Cloud Aiplatform V1beta1Env Var Response] - 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_2to have the valuefoo 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 theenvfield of the Kubernetes Containers v1 core API. - health_
probe GoogleCloud Aiplatform V1beta1Probe Response - 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/barpath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment 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[Google
Cloud Aiplatform V1beta1Port Response] - 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 theportsfield 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/foopath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - 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 GoogleCloud Aiplatform V1beta1Probe Response - 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 DockerCMD's "default parameters" form. If you don't specify this field but do specify the command field, then the command from thecommandfield runs without any additional arguments. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. If you don't specify this field and don't specify thecommandfield, then the container'sENTRYPOINTandCMDdetermine what runs based on their default behavior. See the Docker documentation about howCMDandENTRYPOINTinteract. 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 theargsfield 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'sENTRYPOINTruns, in conjunction with the args field or the container'sCMD, if either exists. If this field is not specified and the container does not have anENTRYPOINT, then refer to the Docker documentation about howCMDandENTRYPOINTinteract. If you specify this field, then you can also specify theargsfield to provide additional arguments for this command. However, if you specify this field, then the container'sCMDis ignored. See the Kubernetes documentation about how thecommandandargsfields interact with a container'sENTRYPOINTandCMD. 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 thecommandfield of the Kubernetes Containers v1 core API. - deployment
Timeout 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_2to have the valuefoo 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 theenvfield of the Kubernetes Containers v1 core API. - health
Probe Property Map - Immutable. Specification for Kubernetes readiness probe. TODO (b/306244185): Revise documentation before exposing.
- health
Route 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/barpath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - image
Uri 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 theportsfield of the Kubernetes Containers v1 core API. - predict
Route 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/foopath on the port of your container specified by the first value of thisModelContainerSpec'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 (followingendpoints/)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 theAIP_ENDPOINT_IDenvironment variable.) * DEPLOYED_MODEL: DeployedModel.id of theDeployedModel. (Vertex AI makes this value available to your container code as theAIP_DEPLOYED_MODEL_IDenvironment variable.) - 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.
- startup
Probe Property Map - Immutable. Specification for Kubernetes startup probe. TODO (b/306244185): Revise documentation before exposing.
GoogleCloudAiplatformV1beta1ModelExportFormatResponse, GoogleCloudAiplatformV1beta1ModelExportFormatResponseArgs
- Exportable
Contents List<string> - The content of this Model that may be exported.
- Exportable
Contents []string - The content of this Model that may be exported.
- exportable
Contents List<String> - The content of this Model that may be exported.
- exportable
Contents string[] - The content of this Model that may be exported.
- exportable_
contents Sequence[str] - The content of this Model that may be exported.
- exportable
Contents List<String> - The content of this Model that may be exported.
GoogleCloudAiplatformV1beta1ModelOriginalModelInfoResponse, GoogleCloudAiplatformV1beta1ModelOriginalModelInfoResponseArgs
- 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}
GoogleCloudAiplatformV1beta1ModelResponse, GoogleCloudAiplatformV1beta1ModelResponseArgs
- Artifact
Uri 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.
- Container
Spec Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Model Container Spec Response - 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 string - Timestamp when this Model was uploaded into Vertex AI.
- Deployed
Models List<Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Deployed Model Ref Response> - 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.
- Display
Name string - 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 Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Encryption Spec Response - 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.
- Explanation
Spec Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Explanation Spec Response - 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.
- Metadata
Artifact 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}. - Metadata
Schema stringUri - 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 Pulumi.Info Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Model Source Info Response - 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.
- Original
Model Pulumi.Info Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Model Original Model Info Response - If this Model is a copy of another Model, this contains info about the original.
- Predict
Schemata Pulumi.Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Predict Schemata Response - The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
- Supported
Deployment List<string>Resources Types - When this Model is deployed, its prediction resources are described by the
prediction_resourcesfield 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 List<Pulumi.Formats Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Model Export Format Response> - The formats in which this Model may be exported. If empty, this Model is not available for export.
- Supported
Input List<string>Storage Formats - 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: *
jsonlThe JSON Lines format, where each instance is a single line. Uses GcsSource. *csvThe 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-recordThe TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. *tf-record-gzipSimilar totf-record, but the file is gzipped. Uses GcsSource. *bigqueryEach instance is a single row in BigQuery. Uses BigQuerySource. *file-listEach line of the file is the location of an instance to process, usesgcs_sourcefield 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 List<string>Storage Formats - 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: *
jsonlThe JSON Lines format, where each prediction is a single line. Uses GcsDestination. *csvThe 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. *bigqueryEach 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 string - The resource name of the TrainingPipeline that uploaded this Model, if any.
- Update
Time string - Timestamp when this Model was most recently updated.
- Version
Aliases 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. - Version
Create stringTime - Timestamp when this version was created.
- Version
Description string - The description of this version.
- Version
Id 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.
- Version
Update stringTime - Timestamp when this version was most recently updated.
- Artifact
Uri 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.
- Container
Spec GoogleCloud Aiplatform V1beta1Model Container Spec Response - 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 string - Timestamp when this Model was uploaded into Vertex AI.
- Deployed
Models []GoogleCloud Aiplatform V1beta1Deployed Model Ref Response - 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.
- Display
Name string - 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 GoogleCloud Aiplatform V1beta1Encryption Spec Response - 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.
- Explanation
Spec GoogleCloud Aiplatform V1beta1Explanation Spec Response - 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.
- Metadata
Artifact 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}. - Metadata
Schema stringUri - 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 GoogleInfo Cloud Aiplatform V1beta1Model Source Info Response - 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.
- Original
Model GoogleInfo Cloud Aiplatform V1beta1Model Original Model Info Response - If this Model is a copy of another Model, this contains info about the original.
- Predict
Schemata GoogleCloud Aiplatform V1beta1Predict Schemata Response - The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
- Supported
Deployment []stringResources Types - When this Model is deployed, its prediction resources are described by the
prediction_resourcesfield 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 []GoogleFormats Cloud Aiplatform V1beta1Model Export Format Response - The formats in which this Model may be exported. If empty, this Model is not available for export.
- Supported
Input []stringStorage Formats - 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: *
jsonlThe JSON Lines format, where each instance is a single line. Uses GcsSource. *csvThe 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-recordThe TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. *tf-record-gzipSimilar totf-record, but the file is gzipped. Uses GcsSource. *bigqueryEach instance is a single row in BigQuery. Uses BigQuerySource. *file-listEach line of the file is the location of an instance to process, usesgcs_sourcefield 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 []stringStorage Formats - 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: *
jsonlThe JSON Lines format, where each prediction is a single line. Uses GcsDestination. *csvThe 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. *bigqueryEach 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 string - The resource name of the TrainingPipeline that uploaded this Model, if any.
- Update
Time string - Timestamp when this Model was most recently updated.
- Version
Aliases []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. - Version
Create stringTime - Timestamp when this version was created.
- Version
Description string - The description of this version.
- Version
Id 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.
- Version
Update stringTime - Timestamp when this version was most recently updated.
- artifact
Uri 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.
- container
Spec GoogleCloud Aiplatform V1beta1Model Container Spec Response - 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 String - Timestamp when this Model was uploaded into Vertex AI.
- deployed
Models List<GoogleCloud Aiplatform V1beta1Deployed Model Ref Response> - 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.
- display
Name String - 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 GoogleCloud Aiplatform V1beta1Encryption Spec Response - 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.
- explanation
Spec GoogleCloud Aiplatform V1beta1Explanation Spec Response - 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.
- metadata
Artifact 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}. - metadata
Schema StringUri - 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 GoogleInfo Cloud Aiplatform V1beta1Model Source Info Response - 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.
- original
Model GoogleInfo Cloud Aiplatform V1beta1Model Original Model Info Response - If this Model is a copy of another Model, this contains info about the original.
- predict
Schemata GoogleCloud Aiplatform V1beta1Predict Schemata Response - The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
- supported
Deployment List<String>Resources Types - When this Model is deployed, its prediction resources are described by the
prediction_resourcesfield 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 List<GoogleFormats Cloud Aiplatform V1beta1Model Export Format Response> - The formats in which this Model may be exported. If empty, this Model is not available for export.
- supported
Input List<String>Storage Formats - 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: *
jsonlThe JSON Lines format, where each instance is a single line. Uses GcsSource. *csvThe 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-recordThe TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. *tf-record-gzipSimilar totf-record, but the file is gzipped. Uses GcsSource. *bigqueryEach instance is a single row in BigQuery. Uses BigQuerySource. *file-listEach line of the file is the location of an instance to process, usesgcs_sourcefield 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 List<String>Storage Formats - 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: *
jsonlThe JSON Lines format, where each prediction is a single line. Uses GcsDestination. *csvThe 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. *bigqueryEach 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 String - The resource name of the TrainingPipeline that uploaded this Model, if any.
- update
Time String - Timestamp when this Model was most recently updated.
- version
Aliases 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. - version
Create StringTime - Timestamp when this version was created.
- version
Description String - The description of this version.
- version
Id 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.
- version
Update StringTime - Timestamp when this version was most recently updated.
- artifact
Uri 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.
- container
Spec GoogleCloud Aiplatform V1beta1Model Container Spec Response - 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 string - Timestamp when this Model was uploaded into Vertex AI.
- deployed
Models GoogleCloud Aiplatform V1beta1Deployed Model Ref Response[] - 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.
- display
Name string - 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 GoogleCloud Aiplatform V1beta1Encryption Spec Response - 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.
- explanation
Spec GoogleCloud Aiplatform V1beta1Explanation Spec Response - 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.
- metadata
Artifact 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}. - metadata
Schema stringUri - 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 GoogleInfo Cloud Aiplatform V1beta1Model Source Info Response - 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.
- original
Model GoogleInfo Cloud Aiplatform V1beta1Model Original Model Info Response - If this Model is a copy of another Model, this contains info about the original.
- predict
Schemata GoogleCloud Aiplatform V1beta1Predict Schemata Response - The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
- supported
Deployment string[]Resources Types - When this Model is deployed, its prediction resources are described by the
prediction_resourcesfield 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 GoogleFormats Cloud Aiplatform V1beta1Model Export Format Response[] - The formats in which this Model may be exported. If empty, this Model is not available for export.
- supported
Input string[]Storage Formats - 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: *
jsonlThe JSON Lines format, where each instance is a single line. Uses GcsSource. *csvThe 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-recordThe TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. *tf-record-gzipSimilar totf-record, but the file is gzipped. Uses GcsSource. *bigqueryEach instance is a single row in BigQuery. Uses BigQuerySource. *file-listEach line of the file is the location of an instance to process, usesgcs_sourcefield 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 string[]Storage Formats - 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: *
jsonlThe JSON Lines format, where each prediction is a single line. Uses GcsDestination. *csvThe 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. *bigqueryEach 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 string - The resource name of the TrainingPipeline that uploaded this Model, if any.
- update
Time string - Timestamp when this Model was most recently updated.
- version
Aliases 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. - version
Create stringTime - Timestamp when this version was created.
- version
Description string - The description of this version.
- version
Id 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.
- version
Update stringTime - 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 GoogleCloud Aiplatform V1beta1Model Container Spec Response - 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[GoogleCloud Aiplatform V1beta1Deployed Model Ref Response] - 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 GoogleCloud Aiplatform V1beta1Encryption Spec Response - 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 GoogleCloud Aiplatform V1beta1Explanation Spec Response - 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_ struri - 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_ Googleinfo Cloud Aiplatform V1beta1Model Source Info Response - 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_ Googleinfo Cloud Aiplatform V1beta1Model Original Model Info Response - If this Model is a copy of another Model, this contains info about the original.
- predict_
schemata GoogleCloud Aiplatform V1beta1Predict Schemata Response - The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
- supported_
deployment_ Sequence[str]resources_ types - When this Model is deployed, its prediction resources are described by the
prediction_resourcesfield 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_ Sequence[Googleformats Cloud Aiplatform V1beta1Model Export Format Response] - The formats in which this Model may be exported. If empty, this Model is not available for export.
- supported_
input_ Sequence[str]storage_ formats - 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: *
jsonlThe JSON Lines format, where each instance is a single line. Uses GcsSource. *csvThe 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-recordThe TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. *tf-record-gzipSimilar totf-record, but the file is gzipped. Uses GcsSource. *bigqueryEach instance is a single row in BigQuery. Uses BigQuerySource. *file-listEach line of the file is the location of an instance to process, usesgcs_sourcefield 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_ Sequence[str]storage_ formats - 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: *
jsonlThe JSON Lines format, where each prediction is a single line. Uses GcsDestination. *csvThe 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. *bigqueryEach 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_ strtime - 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_ strtime - Timestamp when this version was most recently updated.
- artifact
Uri 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.
- container
Spec 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.
- create
Time String - Timestamp when this Model was uploaded into Vertex AI.
- deployed
Models 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.
- display
Name String - 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 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.
- explanation
Spec 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.
- metadata
Artifact 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}. - metadata
Schema StringUri - 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 Property MapInfo - 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.
- original
Model Property MapInfo - If this Model is a copy of another Model, this contains info about the original.
- predict
Schemata Property Map - The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
- supported
Deployment List<String>Resources Types - When this Model is deployed, its prediction resources are described by the
prediction_resourcesfield 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 List<Property Map>Formats - The formats in which this Model may be exported. If empty, this Model is not available for export.
- supported
Input List<String>Storage Formats - 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: *
jsonlThe JSON Lines format, where each instance is a single line. Uses GcsSource. *csvThe 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-recordThe TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. *tf-record-gzipSimilar totf-record, but the file is gzipped. Uses GcsSource. *bigqueryEach instance is a single row in BigQuery. Uses BigQuerySource. *file-listEach line of the file is the location of an instance to process, usesgcs_sourcefield 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 List<String>Storage Formats - 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: *
jsonlThe JSON Lines format, where each prediction is a single line. Uses GcsDestination. *csvThe 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. *bigqueryEach 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 String - The resource name of the TrainingPipeline that uploaded this Model, if any.
- update
Time String - Timestamp when this Model was most recently updated.
- version
Aliases 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. - version
Create StringTime - Timestamp when this version was created.
- version
Description String - The description of this version.
- version
Id 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.
- version
Update StringTime - Timestamp when this version was most recently updated.
GoogleCloudAiplatformV1beta1ModelSourceInfoResponse, GoogleCloudAiplatformV1beta1ModelSourceInfoResponseArgs
- Copy bool
- If this Model is copy of another Model. If true then source_type pertains to the original.
- Source
Type 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 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.
- source
Type 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.
- source
Type 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.
- source
Type String - Type of the model source.
GoogleCloudAiplatformV1beta1Port, GoogleCloudAiplatformV1beta1PortArgs
- 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.
- 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.
- container
Port 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.
- container
Port 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.
- container
Port Number - The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
GoogleCloudAiplatformV1beta1PortResponse, GoogleCloudAiplatformV1beta1PortResponseArgs
- 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.
- 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.
- container
Port 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.
- container
Port 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.
- container
Port Number - The number of the port to expose on the pod's IP address. Must be a valid port number, between 1 and 65535 inclusive.
GoogleCloudAiplatformV1beta1PredefinedSplit, GoogleCloudAiplatformV1beta1PredefinedSplitArgs
- Key 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.
GoogleCloudAiplatformV1beta1PredefinedSplitResponse, GoogleCloudAiplatformV1beta1PredefinedSplitResponseArgs
- 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.
GoogleCloudAiplatformV1beta1PredictSchemata, GoogleCloudAiplatformV1beta1PredictSchemataArgs
- Instance
Schema stringUri - 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 stringUri - 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 stringUri - 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 stringUri - 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 stringUri - 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 stringUri - 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 StringUri - 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 StringUri - 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 StringUri - 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 stringUri - 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 stringUri - 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 stringUri - 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_ struri - 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_ struri - 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_ struri - 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 StringUri - 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 StringUri - 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 StringUri - Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
GoogleCloudAiplatformV1beta1PredictSchemataResponse, GoogleCloudAiplatformV1beta1PredictSchemataResponseArgs
- Instance
Schema stringUri - 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 stringUri - 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 stringUri - 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 stringUri - 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 stringUri - 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 stringUri - 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 StringUri - 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 StringUri - 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 StringUri - 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 stringUri - 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 stringUri - 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 stringUri - 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_ struri - 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_ struri - 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_ struri - 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 StringUri - 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 StringUri - 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 StringUri - Immutable. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Vertex AI. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
GoogleCloudAiplatformV1beta1Presets, GoogleCloudAiplatformV1beta1PresetsArgs
- Modality
Pulumi.
Google Native. Aiplatform. V1Beta1. Google Cloud Aiplatform V1beta1Presets Modality - 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.
Google Native. Aiplatform. V1Beta1. Google Cloud Aiplatform V1beta1Presets Query - Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to
PRECISE.
- Modality
Google
Cloud Aiplatform V1beta1Presets Modality - 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
Google
Cloud Aiplatform V1beta1Presets Query - Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to
PRECISE.
- modality
Google
Cloud Aiplatform V1beta1Presets Modality - 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
Google
Cloud Aiplatform V1beta1Presets Query - Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to
PRECISE.
- modality
Google
Cloud Aiplatform V1beta1Presets Modality - 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
Google
Cloud Aiplatform V1beta1Presets Query - Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to
PRECISE.
- modality
Google
Cloud Aiplatform V1beta1Presets Modality - 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
Google
Cloud Aiplatform V1beta1Presets Query - Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to
PRECISE.
- modality "MODALITY_UNSPECIFIED" | "IMAGE" | "TEXT" | "TABULAR"
- The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
- query "PRECISE" | "FAST"
- Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to
PRECISE.
GoogleCloudAiplatformV1beta1PresetsModality, GoogleCloudAiplatformV1beta1PresetsModalityArgs
- Modality
Unspecified - MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
- Image
- IMAGEIMAGE modality
- Text
- TEXTTEXT modality
- Tabular
- TABULARTABULAR modality
- Google
Cloud Aiplatform V1beta1Presets Modality Modality Unspecified - MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
- Google
Cloud Aiplatform V1beta1Presets Modality Image - IMAGEIMAGE modality
- Google
Cloud Aiplatform V1beta1Presets Modality Text - TEXTTEXT modality
- Google
Cloud Aiplatform V1beta1Presets 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
- MODALITY_UNSPECIFIED
- MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
- IMAGE
- IMAGEIMAGE modality
- TEXT
- TEXTTEXT modality
- TABULAR
- TABULARTABULAR modality
- "MODALITY_UNSPECIFIED"
- MODALITY_UNSPECIFIEDShould not be set. Added as a recommended best practice for enums
- "IMAGE"
- IMAGEIMAGE modality
- "TEXT"
- TEXTTEXT modality
- "TABULAR"
- TABULARTABULAR modality
GoogleCloudAiplatformV1beta1PresetsQuery, GoogleCloudAiplatformV1beta1PresetsQueryArgs
- Precise
- PRECISEMore precise neighbors as a trade-off against slower response.
- Fast
- FASTFaster response as a trade-off against less precise neighbors.
- Google
Cloud Aiplatform V1beta1Presets Query Precise - PRECISEMore precise neighbors as a trade-off against slower response.
- Google
Cloud Aiplatform V1beta1Presets Query 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.
- "PRECISE"
- PRECISEMore precise neighbors as a trade-off against slower response.
- "FAST"
- FASTFaster response as a trade-off against less precise neighbors.
GoogleCloudAiplatformV1beta1PresetsResponse, GoogleCloudAiplatformV1beta1PresetsResponseArgs
- Modality string
- The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
- Query string
- Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to
PRECISE.
- Modality string
- The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
- Query string
- Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to
PRECISE.
- modality String
- The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
- query String
- Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to
PRECISE.
- modality string
- The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
- query string
- Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to
PRECISE.
- modality str
- The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
- query str
- Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to
PRECISE.
- modality String
- The modality of the uploaded model, which automatically configures the distance measurement and feature normalization for the underlying example index and queries. If your model does not precisely fit one of these types, it is okay to choose the closest type.
- query String
- Preset option controlling parameters for speed-precision trade-off when querying for examples. If omitted, defaults to
PRECISE.
GoogleCloudAiplatformV1beta1Probe, GoogleCloudAiplatformV1beta1ProbeArgs
- Exec
Pulumi.
Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Probe Exec Action - 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
Google
Cloud Aiplatform V1beta1Probe Exec Action - 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
Google
Cloud Aiplatform V1beta1Probe Exec Action - Exec specifies the action to take.
- period
Seconds 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'.
- timeout
Seconds 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
Google
Cloud Aiplatform V1beta1Probe Exec Action - Exec specifies the action to take.
- period
Seconds 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'.
- timeout
Seconds 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_
Google
Cloud Aiplatform V1beta1Probe Exec Action - 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.
- period
Seconds 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'.
- timeout
Seconds Number - Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
GoogleCloudAiplatformV1beta1ProbeExecAction, GoogleCloudAiplatformV1beta1ProbeExecActionArgs
- Command List<string>
- Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
- Command []string
- Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
- command List<String>
- Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
- command string[]
- Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
- command Sequence[str]
- Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
- command List<String>
- Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
GoogleCloudAiplatformV1beta1ProbeExecActionResponse, GoogleCloudAiplatformV1beta1ProbeExecActionResponseArgs
- Command List<string>
- Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
- Command []string
- Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
- command List<String>
- Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
- command string[]
- Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
- command Sequence[str]
- Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
- command List<String>
- Command is the command line to execute inside the container, the working directory for the command is root ('/') in the container's filesystem. The command is simply exec'd, it is not run inside a shell, so traditional shell instructions ('|', etc) won't work. To use a shell, you need to explicitly call out to that shell. Exit status of 0 is treated as live/healthy and non-zero is unhealthy.
GoogleCloudAiplatformV1beta1ProbeResponse, GoogleCloudAiplatformV1beta1ProbeResponseArgs
- Exec
Pulumi.
Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Probe Exec Action Response - 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
Google
Cloud Aiplatform V1beta1Probe Exec Action Response - 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
Google
Cloud Aiplatform V1beta1Probe Exec Action Response - Exec specifies the action to take.
- period
Seconds 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'.
- timeout
Seconds 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
Google
Cloud Aiplatform V1beta1Probe Exec Action Response - Exec specifies the action to take.
- period
Seconds 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'.
- timeout
Seconds 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_
Google
Cloud Aiplatform V1beta1Probe Exec Action Response - 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.
- period
Seconds 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'.
- timeout
Seconds Number - Number of seconds after which the probe times out. Defaults to 1 second. Minimum value is 1. Must be greater or equal to period_seconds. Maps to Kubernetes probe argument 'timeoutSeconds'.
GoogleCloudAiplatformV1beta1SampledShapleyAttribution, GoogleCloudAiplatformV1beta1SampledShapleyAttributionArgs
- Path
Count int - 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.
- path
Count Integer - The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
- path
Count 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.
- path
Count Number - The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponse, GoogleCloudAiplatformV1beta1SampledShapleyAttributionResponseArgs
- Path
Count int - 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.
- path
Count Integer - The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
- path
Count 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.
- path
Count Number - The number of feature permutations to consider when approximating the Shapley values. Valid range of its value is [1, 50], inclusively.
GoogleCloudAiplatformV1beta1SmoothGradConfig, GoogleCloudAiplatformV1beta1SmoothGradConfigArgs
- Feature
Noise Pulumi.Sigma Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Feature Noise Sigma - 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 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.
- Noisy
Sample intCount - 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 GoogleSigma Cloud Aiplatform V1beta1Feature Noise Sigma - 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 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.
- Noisy
Sample intCount - 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 GoogleSigma Cloud Aiplatform V1beta1Feature Noise Sigma - 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 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.
- noisy
Sample IntegerCount - 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 GoogleSigma Cloud Aiplatform V1beta1Feature Noise Sigma - 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 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.
- noisy
Sample numberCount - 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_ Googlesigma Cloud Aiplatform V1beta1Feature Noise Sigma - 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_ intcount - 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 Property MapSigma - 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 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.
- noisy
Sample NumberCount - The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
GoogleCloudAiplatformV1beta1SmoothGradConfigResponse, GoogleCloudAiplatformV1beta1SmoothGradConfigResponseArgs
- Feature
Noise Pulumi.Sigma Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Feature Noise Sigma Response - 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 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.
- Noisy
Sample intCount - 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 GoogleSigma Cloud Aiplatform V1beta1Feature Noise Sigma Response - 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 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.
- Noisy
Sample intCount - 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 GoogleSigma Cloud Aiplatform V1beta1Feature Noise Sigma Response - 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 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.
- noisy
Sample IntegerCount - 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 GoogleSigma Cloud Aiplatform V1beta1Feature Noise Sigma Response - 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 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.
- noisy
Sample numberCount - 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_ Googlesigma Cloud Aiplatform V1beta1Feature Noise Sigma Response - 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_ intcount - 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 Property MapSigma - 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 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.
- noisy
Sample NumberCount - The number of gradient samples to use for approximation. The higher this number, the more accurate the gradient is, but the runtime complexity increases by this factor as well. Valid range of its value is [1, 50]. Defaults to 3.
GoogleCloudAiplatformV1beta1StratifiedSplit, GoogleCloudAiplatformV1beta1StratifiedSplitArgs
- Key string
- The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
- Test
Fraction double - The fraction of the input data that is to be used to evaluate the Model.
- Training
Fraction double - The fraction of the input data that is to be used to train the Model.
- Validation
Fraction 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.
- Test
Fraction float64 - The fraction of the input data that is to be used to evaluate the Model.
- Training
Fraction float64 - The fraction of the input data that is to be used to train the Model.
- Validation
Fraction 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.
- test
Fraction Double - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction Double - The fraction of the input data that is to be used to train the Model.
- validation
Fraction 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.
- test
Fraction number - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction number - The fraction of the input data that is to be used to train the Model.
- validation
Fraction 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.
- test
Fraction Number - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction Number - The fraction of the input data that is to be used to train the Model.
- validation
Fraction Number - The fraction of the input data that is to be used to validate the Model.
GoogleCloudAiplatformV1beta1StratifiedSplitResponse, GoogleCloudAiplatformV1beta1StratifiedSplitResponseArgs
- Key string
- The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
- Test
Fraction double - The fraction of the input data that is to be used to evaluate the Model.
- Training
Fraction double - The fraction of the input data that is to be used to train the Model.
- Validation
Fraction 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.
- Test
Fraction float64 - The fraction of the input data that is to be used to evaluate the Model.
- Training
Fraction float64 - The fraction of the input data that is to be used to train the Model.
- Validation
Fraction 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.
- test
Fraction Double - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction Double - The fraction of the input data that is to be used to train the Model.
- validation
Fraction 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.
- test
Fraction number - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction number - The fraction of the input data that is to be used to train the Model.
- validation
Fraction 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.
- test
Fraction Number - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction Number - The fraction of the input data that is to be used to train the Model.
- validation
Fraction Number - The fraction of the input data that is to be used to validate the Model.
GoogleCloudAiplatformV1beta1TimestampSplit, GoogleCloudAiplatformV1beta1TimestampSplitArgs
- 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-timeformat, wheretime-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 double - The fraction of the input data that is to be used to evaluate the Model.
- Training
Fraction double - The fraction of the input data that is to be used to train the Model.
- Validation
Fraction 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-timeformat, wheretime-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 float64 - The fraction of the input data that is to be used to evaluate the Model.
- Training
Fraction float64 - The fraction of the input data that is to be used to train the Model.
- Validation
Fraction 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-timeformat, wheretime-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 Double - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction Double - The fraction of the input data that is to be used to train the Model.
- validation
Fraction 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-timeformat, wheretime-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 number - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction number - The fraction of the input data that is to be used to train the Model.
- validation
Fraction 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-timeformat, wheretime-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-timeformat, wheretime-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 Number - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction Number - The fraction of the input data that is to be used to train the Model.
- validation
Fraction Number - The fraction of the input data that is to be used to validate the Model.
GoogleCloudAiplatformV1beta1TimestampSplitResponse, GoogleCloudAiplatformV1beta1TimestampSplitResponseArgs
- 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-timeformat, wheretime-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 double - The fraction of the input data that is to be used to evaluate the Model.
- Training
Fraction double - The fraction of the input data that is to be used to train the Model.
- Validation
Fraction 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-timeformat, wheretime-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 float64 - The fraction of the input data that is to be used to evaluate the Model.
- Training
Fraction float64 - The fraction of the input data that is to be used to train the Model.
- Validation
Fraction 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-timeformat, wheretime-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 Double - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction Double - The fraction of the input data that is to be used to train the Model.
- validation
Fraction 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-timeformat, wheretime-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 number - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction number - The fraction of the input data that is to be used to train the Model.
- validation
Fraction 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-timeformat, wheretime-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-timeformat, wheretime-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 Number - The fraction of the input data that is to be used to evaluate the Model.
- training
Fraction Number - The fraction of the input data that is to be used to train the Model.
- validation
Fraction Number - The fraction of the input data that is to be used to validate the Model.
GoogleCloudAiplatformV1beta1XraiAttribution, GoogleCloudAiplatformV1beta1XraiAttributionArgs
- 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 Pulumi.Config Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Blur Baseline Config - 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 Pulumi.Config Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Smooth Grad Config - 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 GoogleConfig Cloud Aiplatform V1beta1Blur Baseline Config - 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 GoogleConfig Cloud Aiplatform V1beta1Smooth Grad Config - 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 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.
- blur
Baseline GoogleConfig Cloud Aiplatform V1beta1Blur Baseline Config - 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 GoogleConfig Cloud Aiplatform V1beta1Smooth Grad Config - 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 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 GoogleConfig Cloud Aiplatform V1beta1Blur Baseline Config - 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 GoogleConfig Cloud Aiplatform V1beta1Smooth Grad Config - 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_ Googleconfig Cloud Aiplatform V1beta1Blur Baseline Config - 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_ Googleconfig Cloud Aiplatform V1beta1Smooth Grad Config - 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 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 Property MapConfig - 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 Property MapConfig - Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
GoogleCloudAiplatformV1beta1XraiAttributionResponse, GoogleCloudAiplatformV1beta1XraiAttributionResponseArgs
- Blur
Baseline Pulumi.Config Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Blur Baseline Config Response - 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 Pulumi.Config Google Native. Aiplatform. V1Beta1. Inputs. Google Cloud Aiplatform V1beta1Smooth Grad Config Response - 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 GoogleConfig Cloud Aiplatform V1beta1Blur Baseline Config Response - 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 GoogleConfig Cloud Aiplatform V1beta1Smooth Grad Config Response - 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 GoogleConfig Cloud Aiplatform V1beta1Blur Baseline Config Response - 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 GoogleConfig Cloud Aiplatform V1beta1Smooth Grad Config Response - 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 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.
- blur
Baseline GoogleConfig Cloud Aiplatform V1beta1Blur Baseline Config Response - 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 GoogleConfig Cloud Aiplatform V1beta1Smooth Grad Config Response - 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 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_ Googleconfig Cloud Aiplatform V1beta1Blur Baseline Config Response - 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_ Googleconfig Cloud Aiplatform V1beta1Smooth Grad Config Response - 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 Property MapConfig - 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 Property MapConfig - 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 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<Immutable
Dictionary<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 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