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google-native.aiplatform/v1.HyperparameterTuningJob
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Google Cloud Native is in preview. Google Cloud Classic is fully supported.
Creates a HyperparameterTuningJob Auto-naming is currently not supported for this resource.
Create HyperparameterTuningJob Resource
Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.
Constructor syntax
new HyperparameterTuningJob(name: string, args: HyperparameterTuningJobArgs, opts?: CustomResourceOptions);
@overload
def HyperparameterTuningJob(resource_name: str,
args: HyperparameterTuningJobArgs,
opts: Optional[ResourceOptions] = None)
@overload
def HyperparameterTuningJob(resource_name: str,
opts: Optional[ResourceOptions] = None,
display_name: Optional[str] = None,
max_trial_count: Optional[int] = None,
parallel_trial_count: Optional[int] = None,
study_spec: Optional[GoogleCloudAiplatformV1StudySpecArgs] = None,
trial_job_spec: Optional[GoogleCloudAiplatformV1CustomJobSpecArgs] = None,
encryption_spec: Optional[GoogleCloudAiplatformV1EncryptionSpecArgs] = None,
labels: Optional[Mapping[str, str]] = None,
location: Optional[str] = None,
max_failed_trial_count: Optional[int] = None,
project: Optional[str] = None)
func NewHyperparameterTuningJob(ctx *Context, name string, args HyperparameterTuningJobArgs, opts ...ResourceOption) (*HyperparameterTuningJob, error)
public HyperparameterTuningJob(string name, HyperparameterTuningJobArgs args, CustomResourceOptions? opts = null)
public HyperparameterTuningJob(String name, HyperparameterTuningJobArgs args)
public HyperparameterTuningJob(String name, HyperparameterTuningJobArgs args, CustomResourceOptions options)
type: google-native:aiplatform/v1:HyperparameterTuningJob
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 HyperparameterTuningJobArgs
- 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 HyperparameterTuningJobArgs
- 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 HyperparameterTuningJobArgs
- The arguments to resource properties.
- opts ResourceOption
- Bag of options to control resource's behavior.
- name string
- The unique name of the resource.
- args HyperparameterTuningJobArgs
- The arguments to resource properties.
- opts CustomResourceOptions
- Bag of options to control resource's behavior.
- name String
- The unique name of the resource.
- args HyperparameterTuningJobArgs
- 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 hyperparameterTuningJobResource = new GoogleNative.Aiplatform.V1.HyperparameterTuningJob("hyperparameterTuningJobResource", new()
{
DisplayName = "string",
MaxTrialCount = 0,
ParallelTrialCount = 0,
StudySpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecArgs
{
Metrics = new[]
{
new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecMetricSpecArgs
{
Goal = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1StudySpecMetricSpecGoal.GoalTypeUnspecified,
MetricId = "string",
SafetyConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfigArgs
{
DesiredMinSafeTrialsFraction = 0,
SafetyThreshold = 0,
},
},
},
Parameters = new[]
{
new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecArgs
{
ParameterId = "string",
CategoricalValueSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpecArgs
{
Values = new[]
{
"string",
},
DefaultValue = "string",
},
ConditionalParameterSpecs = new[]
{
new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecArgs
{
ParameterSpec = googleCloudAiplatformV1StudySpecParameterSpec,
ParentCategoricalValues = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionArgs
{
Values = new[]
{
"string",
},
},
ParentDiscreteValues = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionArgs
{
Values = new[]
{
0,
},
},
ParentIntValues = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueConditionArgs
{
Values = new[]
{
"string",
},
},
},
},
DiscreteValueSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpecArgs
{
Values = new[]
{
0,
},
DefaultValue = 0,
},
DoubleValueSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpecArgs
{
MaxValue = 0,
MinValue = 0,
DefaultValue = 0,
},
IntegerValueSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpecArgs
{
MaxValue = "string",
MinValue = "string",
DefaultValue = "string",
},
ScaleType = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1StudySpecParameterSpecScaleType.ScaleTypeUnspecified,
},
},
Algorithm = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1StudySpecAlgorithm.AlgorithmUnspecified,
ConvexAutomatedStoppingSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpecArgs
{
LearningRateParameterName = "string",
MaxStepCount = "string",
MinMeasurementCount = "string",
MinStepCount = "string",
UpdateAllStoppedTrials = false,
UseElapsedDuration = false,
},
DecayCurveStoppingSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpecArgs
{
UseElapsedDuration = false,
},
MeasurementSelectionType = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1StudySpecMeasurementSelectionType.MeasurementSelectionTypeUnspecified,
MedianAutomatedStoppingSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpecArgs
{
UseElapsedDuration = false,
},
ObservationNoise = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1StudySpecObservationNoise.ObservationNoiseUnspecified,
StudyStoppingConfig = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudySpecStudyStoppingConfigArgs
{
MaxDurationNoProgress = "string",
MaxNumTrials = 0,
MaxNumTrialsNoProgress = 0,
MaximumRuntimeConstraint = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudyTimeConstraintArgs
{
EndTime = "string",
MaxDuration = "string",
},
MinNumTrials = 0,
MinimumRuntimeConstraint = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1StudyTimeConstraintArgs
{
EndTime = "string",
MaxDuration = "string",
},
ShouldStopAsap = false,
},
},
TrialJobSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1CustomJobSpecArgs
{
WorkerPoolSpecs = new[]
{
new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1WorkerPoolSpecArgs
{
ContainerSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1ContainerSpecArgs
{
ImageUri = "string",
Args = new[]
{
"string",
},
Command = new[]
{
"string",
},
Env = new[]
{
new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EnvVarArgs
{
Name = "string",
Value = "string",
},
},
},
DiskSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1DiskSpecArgs
{
BootDiskSizeGb = 0,
BootDiskType = "string",
},
MachineSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1MachineSpecArgs
{
AcceleratorCount = 0,
AcceleratorType = GoogleNative.Aiplatform.V1.GoogleCloudAiplatformV1MachineSpecAcceleratorType.AcceleratorTypeUnspecified,
MachineType = "string",
TpuTopology = "string",
},
NfsMounts = new[]
{
new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1NfsMountArgs
{
MountPoint = "string",
Path = "string",
Server = "string",
},
},
PythonPackageSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1PythonPackageSpecArgs
{
ExecutorImageUri = "string",
PackageUris = new[]
{
"string",
},
PythonModule = "string",
Args = new[]
{
"string",
},
Env = new[]
{
new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EnvVarArgs
{
Name = "string",
Value = "string",
},
},
},
ReplicaCount = "string",
},
},
BaseOutputDirectory = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1GcsDestinationArgs
{
OutputUriPrefix = "string",
},
EnableDashboardAccess = false,
EnableWebAccess = false,
Experiment = "string",
ExperimentRun = "string",
Network = "string",
ProtectedArtifactLocationId = "string",
ReservedIpRanges = new[]
{
"string",
},
Scheduling = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1SchedulingArgs
{
DisableRetries = false,
RestartJobOnWorkerRestart = false,
Timeout = "string",
},
ServiceAccount = "string",
Tensorboard = "string",
},
EncryptionSpec = new GoogleNative.Aiplatform.V1.Inputs.GoogleCloudAiplatformV1EncryptionSpecArgs
{
KmsKeyName = "string",
},
Labels =
{
{ "string", "string" },
},
Location = "string",
MaxFailedTrialCount = 0,
Project = "string",
});
example, err := aiplatform.NewHyperparameterTuningJob(ctx, "hyperparameterTuningJobResource", &aiplatform.HyperparameterTuningJobArgs{
DisplayName: pulumi.String("string"),
MaxTrialCount: pulumi.Int(0),
ParallelTrialCount: pulumi.Int(0),
StudySpec: &aiplatform.GoogleCloudAiplatformV1StudySpecArgs{
Metrics: aiplatform.GoogleCloudAiplatformV1StudySpecMetricSpecArray{
&aiplatform.GoogleCloudAiplatformV1StudySpecMetricSpecArgs{
Goal: aiplatform.GoogleCloudAiplatformV1StudySpecMetricSpecGoalGoalTypeUnspecified,
MetricId: pulumi.String("string"),
SafetyConfig: &aiplatform.GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfigArgs{
DesiredMinSafeTrialsFraction: pulumi.Float64(0),
SafetyThreshold: pulumi.Float64(0),
},
},
},
Parameters: aiplatform.GoogleCloudAiplatformV1StudySpecParameterSpecArray{
&aiplatform.GoogleCloudAiplatformV1StudySpecParameterSpecArgs{
ParameterId: pulumi.String("string"),
CategoricalValueSpec: &aiplatform.GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpecArgs{
Values: pulumi.StringArray{
pulumi.String("string"),
},
DefaultValue: pulumi.String("string"),
},
ConditionalParameterSpecs: aiplatform.GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecArray{
&aiplatform.GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecArgs{
ParameterSpec: pulumi.Any(googleCloudAiplatformV1StudySpecParameterSpec),
ParentCategoricalValues: &aiplatform.GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionArgs{
Values: pulumi.StringArray{
pulumi.String("string"),
},
},
ParentDiscreteValues: &aiplatform.GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionArgs{
Values: pulumi.Float64Array{
pulumi.Float64(0),
},
},
ParentIntValues: &aiplatform.GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueConditionArgs{
Values: pulumi.StringArray{
pulumi.String("string"),
},
},
},
},
DiscreteValueSpec: &aiplatform.GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpecArgs{
Values: pulumi.Float64Array{
pulumi.Float64(0),
},
DefaultValue: pulumi.Float64(0),
},
DoubleValueSpec: &aiplatform.GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpecArgs{
MaxValue: pulumi.Float64(0),
MinValue: pulumi.Float64(0),
DefaultValue: pulumi.Float64(0),
},
IntegerValueSpec: &aiplatform.GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpecArgs{
MaxValue: pulumi.String("string"),
MinValue: pulumi.String("string"),
DefaultValue: pulumi.String("string"),
},
ScaleType: aiplatform.GoogleCloudAiplatformV1StudySpecParameterSpecScaleTypeScaleTypeUnspecified,
},
},
Algorithm: aiplatform.GoogleCloudAiplatformV1StudySpecAlgorithmAlgorithmUnspecified,
ConvexAutomatedStoppingSpec: &aiplatform.GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpecArgs{
LearningRateParameterName: pulumi.String("string"),
MaxStepCount: pulumi.String("string"),
MinMeasurementCount: pulumi.String("string"),
MinStepCount: pulumi.String("string"),
UpdateAllStoppedTrials: pulumi.Bool(false),
UseElapsedDuration: pulumi.Bool(false),
},
DecayCurveStoppingSpec: &aiplatform.GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpecArgs{
UseElapsedDuration: pulumi.Bool(false),
},
MeasurementSelectionType: aiplatform.GoogleCloudAiplatformV1StudySpecMeasurementSelectionTypeMeasurementSelectionTypeUnspecified,
MedianAutomatedStoppingSpec: &aiplatform.GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpecArgs{
UseElapsedDuration: pulumi.Bool(false),
},
ObservationNoise: aiplatform.GoogleCloudAiplatformV1StudySpecObservationNoiseObservationNoiseUnspecified,
StudyStoppingConfig: &aiplatform.GoogleCloudAiplatformV1StudySpecStudyStoppingConfigArgs{
MaxDurationNoProgress: pulumi.String("string"),
MaxNumTrials: pulumi.Int(0),
MaxNumTrialsNoProgress: pulumi.Int(0),
MaximumRuntimeConstraint: &aiplatform.GoogleCloudAiplatformV1StudyTimeConstraintArgs{
EndTime: pulumi.String("string"),
MaxDuration: pulumi.String("string"),
},
MinNumTrials: pulumi.Int(0),
MinimumRuntimeConstraint: &aiplatform.GoogleCloudAiplatformV1StudyTimeConstraintArgs{
EndTime: pulumi.String("string"),
MaxDuration: pulumi.String("string"),
},
ShouldStopAsap: pulumi.Bool(false),
},
},
TrialJobSpec: &aiplatform.GoogleCloudAiplatformV1CustomJobSpecArgs{
WorkerPoolSpecs: aiplatform.GoogleCloudAiplatformV1WorkerPoolSpecArray{
&aiplatform.GoogleCloudAiplatformV1WorkerPoolSpecArgs{
ContainerSpec: &aiplatform.GoogleCloudAiplatformV1ContainerSpecArgs{
ImageUri: pulumi.String("string"),
Args: pulumi.StringArray{
pulumi.String("string"),
},
Command: pulumi.StringArray{
pulumi.String("string"),
},
Env: aiplatform.GoogleCloudAiplatformV1EnvVarArray{
&aiplatform.GoogleCloudAiplatformV1EnvVarArgs{
Name: pulumi.String("string"),
Value: pulumi.String("string"),
},
},
},
DiskSpec: &aiplatform.GoogleCloudAiplatformV1DiskSpecArgs{
BootDiskSizeGb: pulumi.Int(0),
BootDiskType: pulumi.String("string"),
},
MachineSpec: &aiplatform.GoogleCloudAiplatformV1MachineSpecArgs{
AcceleratorCount: pulumi.Int(0),
AcceleratorType: aiplatform.GoogleCloudAiplatformV1MachineSpecAcceleratorTypeAcceleratorTypeUnspecified,
MachineType: pulumi.String("string"),
TpuTopology: pulumi.String("string"),
},
NfsMounts: aiplatform.GoogleCloudAiplatformV1NfsMountArray{
&aiplatform.GoogleCloudAiplatformV1NfsMountArgs{
MountPoint: pulumi.String("string"),
Path: pulumi.String("string"),
Server: pulumi.String("string"),
},
},
PythonPackageSpec: &aiplatform.GoogleCloudAiplatformV1PythonPackageSpecArgs{
ExecutorImageUri: pulumi.String("string"),
PackageUris: pulumi.StringArray{
pulumi.String("string"),
},
PythonModule: pulumi.String("string"),
Args: pulumi.StringArray{
pulumi.String("string"),
},
Env: aiplatform.GoogleCloudAiplatformV1EnvVarArray{
&aiplatform.GoogleCloudAiplatformV1EnvVarArgs{
Name: pulumi.String("string"),
Value: pulumi.String("string"),
},
},
},
ReplicaCount: pulumi.String("string"),
},
},
BaseOutputDirectory: &aiplatform.GoogleCloudAiplatformV1GcsDestinationArgs{
OutputUriPrefix: pulumi.String("string"),
},
EnableDashboardAccess: pulumi.Bool(false),
EnableWebAccess: pulumi.Bool(false),
Experiment: pulumi.String("string"),
ExperimentRun: pulumi.String("string"),
Network: pulumi.String("string"),
ProtectedArtifactLocationId: pulumi.String("string"),
ReservedIpRanges: pulumi.StringArray{
pulumi.String("string"),
},
Scheduling: &aiplatform.GoogleCloudAiplatformV1SchedulingArgs{
DisableRetries: pulumi.Bool(false),
RestartJobOnWorkerRestart: pulumi.Bool(false),
Timeout: pulumi.String("string"),
},
ServiceAccount: pulumi.String("string"),
Tensorboard: pulumi.String("string"),
},
EncryptionSpec: &aiplatform.GoogleCloudAiplatformV1EncryptionSpecArgs{
KmsKeyName: pulumi.String("string"),
},
Labels: pulumi.StringMap{
"string": pulumi.String("string"),
},
Location: pulumi.String("string"),
MaxFailedTrialCount: pulumi.Int(0),
Project: pulumi.String("string"),
})
var hyperparameterTuningJobResource = new HyperparameterTuningJob("hyperparameterTuningJobResource", HyperparameterTuningJobArgs.builder()
.displayName("string")
.maxTrialCount(0)
.parallelTrialCount(0)
.studySpec(GoogleCloudAiplatformV1StudySpecArgs.builder()
.metrics(GoogleCloudAiplatformV1StudySpecMetricSpecArgs.builder()
.goal("GOAL_TYPE_UNSPECIFIED")
.metricId("string")
.safetyConfig(GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfigArgs.builder()
.desiredMinSafeTrialsFraction(0)
.safetyThreshold(0)
.build())
.build())
.parameters(GoogleCloudAiplatformV1StudySpecParameterSpecArgs.builder()
.parameterId("string")
.categoricalValueSpec(GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpecArgs.builder()
.values("string")
.defaultValue("string")
.build())
.conditionalParameterSpecs(GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecArgs.builder()
.parameterSpec(googleCloudAiplatformV1StudySpecParameterSpec)
.parentCategoricalValues(GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionArgs.builder()
.values("string")
.build())
.parentDiscreteValues(GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionArgs.builder()
.values(0)
.build())
.parentIntValues(GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueConditionArgs.builder()
.values("string")
.build())
.build())
.discreteValueSpec(GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpecArgs.builder()
.values(0)
.defaultValue(0)
.build())
.doubleValueSpec(GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpecArgs.builder()
.maxValue(0)
.minValue(0)
.defaultValue(0)
.build())
.integerValueSpec(GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpecArgs.builder()
.maxValue("string")
.minValue("string")
.defaultValue("string")
.build())
.scaleType("SCALE_TYPE_UNSPECIFIED")
.build())
.algorithm("ALGORITHM_UNSPECIFIED")
.convexAutomatedStoppingSpec(GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpecArgs.builder()
.learningRateParameterName("string")
.maxStepCount("string")
.minMeasurementCount("string")
.minStepCount("string")
.updateAllStoppedTrials(false)
.useElapsedDuration(false)
.build())
.decayCurveStoppingSpec(GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpecArgs.builder()
.useElapsedDuration(false)
.build())
.measurementSelectionType("MEASUREMENT_SELECTION_TYPE_UNSPECIFIED")
.medianAutomatedStoppingSpec(GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpecArgs.builder()
.useElapsedDuration(false)
.build())
.observationNoise("OBSERVATION_NOISE_UNSPECIFIED")
.studyStoppingConfig(GoogleCloudAiplatformV1StudySpecStudyStoppingConfigArgs.builder()
.maxDurationNoProgress("string")
.maxNumTrials(0)
.maxNumTrialsNoProgress(0)
.maximumRuntimeConstraint(GoogleCloudAiplatformV1StudyTimeConstraintArgs.builder()
.endTime("string")
.maxDuration("string")
.build())
.minNumTrials(0)
.minimumRuntimeConstraint(GoogleCloudAiplatformV1StudyTimeConstraintArgs.builder()
.endTime("string")
.maxDuration("string")
.build())
.shouldStopAsap(false)
.build())
.build())
.trialJobSpec(GoogleCloudAiplatformV1CustomJobSpecArgs.builder()
.workerPoolSpecs(GoogleCloudAiplatformV1WorkerPoolSpecArgs.builder()
.containerSpec(GoogleCloudAiplatformV1ContainerSpecArgs.builder()
.imageUri("string")
.args("string")
.command("string")
.env(GoogleCloudAiplatformV1EnvVarArgs.builder()
.name("string")
.value("string")
.build())
.build())
.diskSpec(GoogleCloudAiplatformV1DiskSpecArgs.builder()
.bootDiskSizeGb(0)
.bootDiskType("string")
.build())
.machineSpec(GoogleCloudAiplatformV1MachineSpecArgs.builder()
.acceleratorCount(0)
.acceleratorType("ACCELERATOR_TYPE_UNSPECIFIED")
.machineType("string")
.tpuTopology("string")
.build())
.nfsMounts(GoogleCloudAiplatformV1NfsMountArgs.builder()
.mountPoint("string")
.path("string")
.server("string")
.build())
.pythonPackageSpec(GoogleCloudAiplatformV1PythonPackageSpecArgs.builder()
.executorImageUri("string")
.packageUris("string")
.pythonModule("string")
.args("string")
.env(GoogleCloudAiplatformV1EnvVarArgs.builder()
.name("string")
.value("string")
.build())
.build())
.replicaCount("string")
.build())
.baseOutputDirectory(GoogleCloudAiplatformV1GcsDestinationArgs.builder()
.outputUriPrefix("string")
.build())
.enableDashboardAccess(false)
.enableWebAccess(false)
.experiment("string")
.experimentRun("string")
.network("string")
.protectedArtifactLocationId("string")
.reservedIpRanges("string")
.scheduling(GoogleCloudAiplatformV1SchedulingArgs.builder()
.disableRetries(false)
.restartJobOnWorkerRestart(false)
.timeout("string")
.build())
.serviceAccount("string")
.tensorboard("string")
.build())
.encryptionSpec(GoogleCloudAiplatformV1EncryptionSpecArgs.builder()
.kmsKeyName("string")
.build())
.labels(Map.of("string", "string"))
.location("string")
.maxFailedTrialCount(0)
.project("string")
.build());
hyperparameter_tuning_job_resource = google_native.aiplatform.v1.HyperparameterTuningJob("hyperparameterTuningJobResource",
display_name="string",
max_trial_count=0,
parallel_trial_count=0,
study_spec={
"metrics": [{
"goal": google_native.aiplatform.v1.GoogleCloudAiplatformV1StudySpecMetricSpecGoal.GOAL_TYPE_UNSPECIFIED,
"metric_id": "string",
"safety_config": {
"desired_min_safe_trials_fraction": 0,
"safety_threshold": 0,
},
}],
"parameters": [{
"parameter_id": "string",
"categorical_value_spec": {
"values": ["string"],
"default_value": "string",
},
"conditional_parameter_specs": [{
"parameter_spec": google_cloud_aiplatform_v1_study_spec_parameter_spec,
"parent_categorical_values": {
"values": ["string"],
},
"parent_discrete_values": {
"values": [0],
},
"parent_int_values": {
"values": ["string"],
},
}],
"discrete_value_spec": {
"values": [0],
"default_value": 0,
},
"double_value_spec": {
"max_value": 0,
"min_value": 0,
"default_value": 0,
},
"integer_value_spec": {
"max_value": "string",
"min_value": "string",
"default_value": "string",
},
"scale_type": google_native.aiplatform.v1.GoogleCloudAiplatformV1StudySpecParameterSpecScaleType.SCALE_TYPE_UNSPECIFIED,
}],
"algorithm": google_native.aiplatform.v1.GoogleCloudAiplatformV1StudySpecAlgorithm.ALGORITHM_UNSPECIFIED,
"convex_automated_stopping_spec": {
"learning_rate_parameter_name": "string",
"max_step_count": "string",
"min_measurement_count": "string",
"min_step_count": "string",
"update_all_stopped_trials": False,
"use_elapsed_duration": False,
},
"decay_curve_stopping_spec": {
"use_elapsed_duration": False,
},
"measurement_selection_type": google_native.aiplatform.v1.GoogleCloudAiplatformV1StudySpecMeasurementSelectionType.MEASUREMENT_SELECTION_TYPE_UNSPECIFIED,
"median_automated_stopping_spec": {
"use_elapsed_duration": False,
},
"observation_noise": google_native.aiplatform.v1.GoogleCloudAiplatformV1StudySpecObservationNoise.OBSERVATION_NOISE_UNSPECIFIED,
"study_stopping_config": {
"max_duration_no_progress": "string",
"max_num_trials": 0,
"max_num_trials_no_progress": 0,
"maximum_runtime_constraint": {
"end_time": "string",
"max_duration": "string",
},
"min_num_trials": 0,
"minimum_runtime_constraint": {
"end_time": "string",
"max_duration": "string",
},
"should_stop_asap": False,
},
},
trial_job_spec={
"worker_pool_specs": [{
"container_spec": {
"image_uri": "string",
"args": ["string"],
"command": ["string"],
"env": [{
"name": "string",
"value": "string",
}],
},
"disk_spec": {
"boot_disk_size_gb": 0,
"boot_disk_type": "string",
},
"machine_spec": {
"accelerator_count": 0,
"accelerator_type": google_native.aiplatform.v1.GoogleCloudAiplatformV1MachineSpecAcceleratorType.ACCELERATOR_TYPE_UNSPECIFIED,
"machine_type": "string",
"tpu_topology": "string",
},
"nfs_mounts": [{
"mount_point": "string",
"path": "string",
"server": "string",
}],
"python_package_spec": {
"executor_image_uri": "string",
"package_uris": ["string"],
"python_module": "string",
"args": ["string"],
"env": [{
"name": "string",
"value": "string",
}],
},
"replica_count": "string",
}],
"base_output_directory": {
"output_uri_prefix": "string",
},
"enable_dashboard_access": False,
"enable_web_access": False,
"experiment": "string",
"experiment_run": "string",
"network": "string",
"protected_artifact_location_id": "string",
"reserved_ip_ranges": ["string"],
"scheduling": {
"disable_retries": False,
"restart_job_on_worker_restart": False,
"timeout": "string",
},
"service_account": "string",
"tensorboard": "string",
},
encryption_spec={
"kms_key_name": "string",
},
labels={
"string": "string",
},
location="string",
max_failed_trial_count=0,
project="string")
const hyperparameterTuningJobResource = new google_native.aiplatform.v1.HyperparameterTuningJob("hyperparameterTuningJobResource", {
displayName: "string",
maxTrialCount: 0,
parallelTrialCount: 0,
studySpec: {
metrics: [{
goal: google_native.aiplatform.v1.GoogleCloudAiplatformV1StudySpecMetricSpecGoal.GoalTypeUnspecified,
metricId: "string",
safetyConfig: {
desiredMinSafeTrialsFraction: 0,
safetyThreshold: 0,
},
}],
parameters: [{
parameterId: "string",
categoricalValueSpec: {
values: ["string"],
defaultValue: "string",
},
conditionalParameterSpecs: [{
parameterSpec: googleCloudAiplatformV1StudySpecParameterSpec,
parentCategoricalValues: {
values: ["string"],
},
parentDiscreteValues: {
values: [0],
},
parentIntValues: {
values: ["string"],
},
}],
discreteValueSpec: {
values: [0],
defaultValue: 0,
},
doubleValueSpec: {
maxValue: 0,
minValue: 0,
defaultValue: 0,
},
integerValueSpec: {
maxValue: "string",
minValue: "string",
defaultValue: "string",
},
scaleType: google_native.aiplatform.v1.GoogleCloudAiplatformV1StudySpecParameterSpecScaleType.ScaleTypeUnspecified,
}],
algorithm: google_native.aiplatform.v1.GoogleCloudAiplatformV1StudySpecAlgorithm.AlgorithmUnspecified,
convexAutomatedStoppingSpec: {
learningRateParameterName: "string",
maxStepCount: "string",
minMeasurementCount: "string",
minStepCount: "string",
updateAllStoppedTrials: false,
useElapsedDuration: false,
},
decayCurveStoppingSpec: {
useElapsedDuration: false,
},
measurementSelectionType: google_native.aiplatform.v1.GoogleCloudAiplatformV1StudySpecMeasurementSelectionType.MeasurementSelectionTypeUnspecified,
medianAutomatedStoppingSpec: {
useElapsedDuration: false,
},
observationNoise: google_native.aiplatform.v1.GoogleCloudAiplatformV1StudySpecObservationNoise.ObservationNoiseUnspecified,
studyStoppingConfig: {
maxDurationNoProgress: "string",
maxNumTrials: 0,
maxNumTrialsNoProgress: 0,
maximumRuntimeConstraint: {
endTime: "string",
maxDuration: "string",
},
minNumTrials: 0,
minimumRuntimeConstraint: {
endTime: "string",
maxDuration: "string",
},
shouldStopAsap: false,
},
},
trialJobSpec: {
workerPoolSpecs: [{
containerSpec: {
imageUri: "string",
args: ["string"],
command: ["string"],
env: [{
name: "string",
value: "string",
}],
},
diskSpec: {
bootDiskSizeGb: 0,
bootDiskType: "string",
},
machineSpec: {
acceleratorCount: 0,
acceleratorType: google_native.aiplatform.v1.GoogleCloudAiplatformV1MachineSpecAcceleratorType.AcceleratorTypeUnspecified,
machineType: "string",
tpuTopology: "string",
},
nfsMounts: [{
mountPoint: "string",
path: "string",
server: "string",
}],
pythonPackageSpec: {
executorImageUri: "string",
packageUris: ["string"],
pythonModule: "string",
args: ["string"],
env: [{
name: "string",
value: "string",
}],
},
replicaCount: "string",
}],
baseOutputDirectory: {
outputUriPrefix: "string",
},
enableDashboardAccess: false,
enableWebAccess: false,
experiment: "string",
experimentRun: "string",
network: "string",
protectedArtifactLocationId: "string",
reservedIpRanges: ["string"],
scheduling: {
disableRetries: false,
restartJobOnWorkerRestart: false,
timeout: "string",
},
serviceAccount: "string",
tensorboard: "string",
},
encryptionSpec: {
kmsKeyName: "string",
},
labels: {
string: "string",
},
location: "string",
maxFailedTrialCount: 0,
project: "string",
});
type: google-native:aiplatform/v1:HyperparameterTuningJob
properties:
displayName: string
encryptionSpec:
kmsKeyName: string
labels:
string: string
location: string
maxFailedTrialCount: 0
maxTrialCount: 0
parallelTrialCount: 0
project: string
studySpec:
algorithm: ALGORITHM_UNSPECIFIED
convexAutomatedStoppingSpec:
learningRateParameterName: string
maxStepCount: string
minMeasurementCount: string
minStepCount: string
updateAllStoppedTrials: false
useElapsedDuration: false
decayCurveStoppingSpec:
useElapsedDuration: false
measurementSelectionType: MEASUREMENT_SELECTION_TYPE_UNSPECIFIED
medianAutomatedStoppingSpec:
useElapsedDuration: false
metrics:
- goal: GOAL_TYPE_UNSPECIFIED
metricId: string
safetyConfig:
desiredMinSafeTrialsFraction: 0
safetyThreshold: 0
observationNoise: OBSERVATION_NOISE_UNSPECIFIED
parameters:
- categoricalValueSpec:
defaultValue: string
values:
- string
conditionalParameterSpecs:
- parameterSpec: ${googleCloudAiplatformV1StudySpecParameterSpec}
parentCategoricalValues:
values:
- string
parentDiscreteValues:
values:
- 0
parentIntValues:
values:
- string
discreteValueSpec:
defaultValue: 0
values:
- 0
doubleValueSpec:
defaultValue: 0
maxValue: 0
minValue: 0
integerValueSpec:
defaultValue: string
maxValue: string
minValue: string
parameterId: string
scaleType: SCALE_TYPE_UNSPECIFIED
studyStoppingConfig:
maxDurationNoProgress: string
maxNumTrials: 0
maxNumTrialsNoProgress: 0
maximumRuntimeConstraint:
endTime: string
maxDuration: string
minNumTrials: 0
minimumRuntimeConstraint:
endTime: string
maxDuration: string
shouldStopAsap: false
trialJobSpec:
baseOutputDirectory:
outputUriPrefix: string
enableDashboardAccess: false
enableWebAccess: false
experiment: string
experimentRun: string
network: string
protectedArtifactLocationId: string
reservedIpRanges:
- string
scheduling:
disableRetries: false
restartJobOnWorkerRestart: false
timeout: string
serviceAccount: string
tensorboard: string
workerPoolSpecs:
- containerSpec:
args:
- string
command:
- string
env:
- name: string
value: string
imageUri: string
diskSpec:
bootDiskSizeGb: 0
bootDiskType: string
machineSpec:
acceleratorCount: 0
acceleratorType: ACCELERATOR_TYPE_UNSPECIFIED
machineType: string
tpuTopology: string
nfsMounts:
- mountPoint: string
path: string
server: string
pythonPackageSpec:
args:
- string
env:
- name: string
value: string
executorImageUri: string
packageUris:
- string
pythonModule: string
replicaCount: string
HyperparameterTuningJob Resource Properties
To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.
Inputs
In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.
The HyperparameterTuningJob resource accepts the following input properties:
- Display
Name string - The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- Max
Trial intCount - The desired total number of Trials.
- Parallel
Trial intCount - The desired number of Trials to run in parallel.
- Study
Spec Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec - Study configuration of the HyperparameterTuningJob.
- Trial
Job Pulumi.Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Custom Job Spec - The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
- Encryption
Spec Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Encryption Spec - Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
- Labels Dictionary<string, string>
- The labels with user-defined metadata to organize HyperparameterTuningJobs. 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
- Max
Failed intTrial Count - The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
- Project string
- Display
Name string - The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- Max
Trial intCount - The desired total number of Trials.
- Parallel
Trial intCount - The desired number of Trials to run in parallel.
- Study
Spec GoogleCloud Aiplatform V1Study Spec Args - Study configuration of the HyperparameterTuningJob.
- Trial
Job GoogleSpec Cloud Aiplatform V1Custom Job Spec Args - The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
- Encryption
Spec GoogleCloud Aiplatform V1Encryption Spec Args - Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
- Labels map[string]string
- The labels with user-defined metadata to organize HyperparameterTuningJobs. 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
- Max
Failed intTrial Count - The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
- Project string
- display
Name String - The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- max
Trial IntegerCount - The desired total number of Trials.
- parallel
Trial IntegerCount - The desired number of Trials to run in parallel.
- study
Spec GoogleCloud Aiplatform V1Study Spec - Study configuration of the HyperparameterTuningJob.
- trial
Job GoogleSpec Cloud Aiplatform V1Custom Job Spec - The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
- encryption
Spec GoogleCloud Aiplatform V1Encryption Spec - Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
- labels Map<String,String>
- The labels with user-defined metadata to organize HyperparameterTuningJobs. 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
- max
Failed IntegerTrial Count - The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
- project String
- display
Name string - The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- max
Trial numberCount - The desired total number of Trials.
- parallel
Trial numberCount - The desired number of Trials to run in parallel.
- study
Spec GoogleCloud Aiplatform V1Study Spec - Study configuration of the HyperparameterTuningJob.
- trial
Job GoogleSpec Cloud Aiplatform V1Custom Job Spec - The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
- encryption
Spec GoogleCloud Aiplatform V1Encryption Spec - Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
- labels {[key: string]: string}
- The labels with user-defined metadata to organize HyperparameterTuningJobs. 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
- max
Failed numberTrial Count - The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
- project string
- display_
name str - The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- max_
trial_ intcount - The desired total number of Trials.
- parallel_
trial_ intcount - The desired number of Trials to run in parallel.
- study_
spec GoogleCloud Aiplatform V1Study Spec Args - Study configuration of the HyperparameterTuningJob.
- trial_
job_ Googlespec Cloud Aiplatform V1Custom Job Spec Args - The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
- encryption_
spec GoogleCloud Aiplatform V1Encryption Spec Args - Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
- labels Mapping[str, str]
- The labels with user-defined metadata to organize HyperparameterTuningJobs. 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
- max_
failed_ inttrial_ count - The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
- project str
- display
Name String - The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
- max
Trial NumberCount - The desired total number of Trials.
- parallel
Trial NumberCount - The desired number of Trials to run in parallel.
- study
Spec Property Map - Study configuration of the HyperparameterTuningJob.
- trial
Job Property MapSpec - The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
- encryption
Spec Property Map - Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
- labels Map<String>
- The labels with user-defined metadata to organize HyperparameterTuningJobs. 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
- max
Failed NumberTrial Count - The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
- project String
Outputs
All input properties are implicitly available as output properties. Additionally, the HyperparameterTuningJob resource produces the following output properties:
- Create
Time string - Time when the HyperparameterTuningJob was created.
- End
Time string - Time when the HyperparameterTuningJob entered any of the following states:
JOB_STATE_SUCCEEDED
,JOB_STATE_FAILED
,JOB_STATE_CANCELLED
. - Error
Pulumi.
Google Native. Aiplatform. V1. Outputs. Google Rpc Status Response - Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
- Id string
- The provider-assigned unique ID for this managed resource.
- Name string
- Resource name of the HyperparameterTuningJob.
- Start
Time string - Time when the HyperparameterTuningJob for the first time entered the
JOB_STATE_RUNNING
state. - State string
- The detailed state of the job.
- Trials
List<Pulumi.
Google Native. Aiplatform. V1. Outputs. Google Cloud Aiplatform V1Trial Response> - Trials of the HyperparameterTuningJob.
- Update
Time string - Time when the HyperparameterTuningJob was most recently updated.
- Create
Time string - Time when the HyperparameterTuningJob was created.
- End
Time string - Time when the HyperparameterTuningJob entered any of the following states:
JOB_STATE_SUCCEEDED
,JOB_STATE_FAILED
,JOB_STATE_CANCELLED
. - Error
Google
Rpc Status Response - Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
- Id string
- The provider-assigned unique ID for this managed resource.
- Name string
- Resource name of the HyperparameterTuningJob.
- Start
Time string - Time when the HyperparameterTuningJob for the first time entered the
JOB_STATE_RUNNING
state. - State string
- The detailed state of the job.
- Trials
[]Google
Cloud Aiplatform V1Trial Response - Trials of the HyperparameterTuningJob.
- Update
Time string - Time when the HyperparameterTuningJob was most recently updated.
- create
Time String - Time when the HyperparameterTuningJob was created.
- end
Time String - Time when the HyperparameterTuningJob entered any of the following states:
JOB_STATE_SUCCEEDED
,JOB_STATE_FAILED
,JOB_STATE_CANCELLED
. - error
Google
Rpc Status Response - Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
- id String
- The provider-assigned unique ID for this managed resource.
- name String
- Resource name of the HyperparameterTuningJob.
- start
Time String - Time when the HyperparameterTuningJob for the first time entered the
JOB_STATE_RUNNING
state. - state String
- The detailed state of the job.
- trials
List<Google
Cloud Aiplatform V1Trial Response> - Trials of the HyperparameterTuningJob.
- update
Time String - Time when the HyperparameterTuningJob was most recently updated.
- create
Time string - Time when the HyperparameterTuningJob was created.
- end
Time string - Time when the HyperparameterTuningJob entered any of the following states:
JOB_STATE_SUCCEEDED
,JOB_STATE_FAILED
,JOB_STATE_CANCELLED
. - error
Google
Rpc Status Response - Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
- id string
- The provider-assigned unique ID for this managed resource.
- name string
- Resource name of the HyperparameterTuningJob.
- start
Time string - Time when the HyperparameterTuningJob for the first time entered the
JOB_STATE_RUNNING
state. - state string
- The detailed state of the job.
- trials
Google
Cloud Aiplatform V1Trial Response[] - Trials of the HyperparameterTuningJob.
- update
Time string - Time when the HyperparameterTuningJob was most recently updated.
- create_
time str - Time when the HyperparameterTuningJob was created.
- end_
time str - Time when the HyperparameterTuningJob entered any of the following states:
JOB_STATE_SUCCEEDED
,JOB_STATE_FAILED
,JOB_STATE_CANCELLED
. - error
Google
Rpc Status Response - Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
- id str
- The provider-assigned unique ID for this managed resource.
- name str
- Resource name of the HyperparameterTuningJob.
- start_
time str - Time when the HyperparameterTuningJob for the first time entered the
JOB_STATE_RUNNING
state. - state str
- The detailed state of the job.
- trials
Sequence[Google
Cloud Aiplatform V1Trial Response] - Trials of the HyperparameterTuningJob.
- update_
time str - Time when the HyperparameterTuningJob was most recently updated.
- create
Time String - Time when the HyperparameterTuningJob was created.
- end
Time String - Time when the HyperparameterTuningJob entered any of the following states:
JOB_STATE_SUCCEEDED
,JOB_STATE_FAILED
,JOB_STATE_CANCELLED
. - error Property Map
- Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
- id String
- The provider-assigned unique ID for this managed resource.
- name String
- Resource name of the HyperparameterTuningJob.
- start
Time String - Time when the HyperparameterTuningJob for the first time entered the
JOB_STATE_RUNNING
state. - state String
- The detailed state of the job.
- trials List<Property Map>
- Trials of the HyperparameterTuningJob.
- update
Time String - Time when the HyperparameterTuningJob was most recently updated.
Supporting Types
GoogleCloudAiplatformV1ContainerSpec, GoogleCloudAiplatformV1ContainerSpecArgs
- Image
Uri string - The URI of a container image in the Container Registry that is to be run on each worker replica.
- Args List<string>
- The arguments to be passed when starting the container.
- Command List<string>
- The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
- Env
List<Pulumi.
Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Env Var> - Environment variables to be passed to the container. Maximum limit is 100.
- Image
Uri string - The URI of a container image in the Container Registry that is to be run on each worker replica.
- Args []string
- The arguments to be passed when starting the container.
- Command []string
- The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
- Env
[]Google
Cloud Aiplatform V1Env Var - Environment variables to be passed to the container. Maximum limit is 100.
- image
Uri String - The URI of a container image in the Container Registry that is to be run on each worker replica.
- args List<String>
- The arguments to be passed when starting the container.
- command List<String>
- The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
- env
List<Google
Cloud Aiplatform V1Env Var> - Environment variables to be passed to the container. Maximum limit is 100.
- image
Uri string - The URI of a container image in the Container Registry that is to be run on each worker replica.
- args string[]
- The arguments to be passed when starting the container.
- command string[]
- The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
- env
Google
Cloud Aiplatform V1Env Var[] - Environment variables to be passed to the container. Maximum limit is 100.
- image_
uri str - The URI of a container image in the Container Registry that is to be run on each worker replica.
- args Sequence[str]
- The arguments to be passed when starting the container.
- command Sequence[str]
- The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
- env
Sequence[Google
Cloud Aiplatform V1Env Var] - Environment variables to be passed to the container. Maximum limit is 100.
- image
Uri String - The URI of a container image in the Container Registry that is to be run on each worker replica.
- args List<String>
- The arguments to be passed when starting the container.
- command List<String>
- The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
- env List<Property Map>
- Environment variables to be passed to the container. Maximum limit is 100.
GoogleCloudAiplatformV1ContainerSpecResponse, GoogleCloudAiplatformV1ContainerSpecResponseArgs
- Args List<string>
- The arguments to be passed when starting the container.
- Command List<string>
- The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
- Env
List<Pulumi.
Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Env Var Response> - Environment variables to be passed to the container. Maximum limit is 100.
- Image
Uri string - The URI of a container image in the Container Registry that is to be run on each worker replica.
- Args []string
- The arguments to be passed when starting the container.
- Command []string
- The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
- Env
[]Google
Cloud Aiplatform V1Env Var Response - Environment variables to be passed to the container. Maximum limit is 100.
- Image
Uri string - The URI of a container image in the Container Registry that is to be run on each worker replica.
- args List<String>
- The arguments to be passed when starting the container.
- command List<String>
- The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
- env
List<Google
Cloud Aiplatform V1Env Var Response> - Environment variables to be passed to the container. Maximum limit is 100.
- image
Uri String - The URI of a container image in the Container Registry that is to be run on each worker replica.
- args string[]
- The arguments to be passed when starting the container.
- command string[]
- The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
- env
Google
Cloud Aiplatform V1Env Var Response[] - Environment variables to be passed to the container. Maximum limit is 100.
- image
Uri string - The URI of a container image in the Container Registry that is to be run on each worker replica.
- args Sequence[str]
- The arguments to be passed when starting the container.
- command Sequence[str]
- The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
- env
Sequence[Google
Cloud Aiplatform V1Env Var Response] - Environment variables to be passed to the container. Maximum limit is 100.
- image_
uri str - The URI of a container image in the Container Registry that is to be run on each worker replica.
- args List<String>
- The arguments to be passed when starting the container.
- command List<String>
- The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
- env List<Property Map>
- Environment variables to be passed to the container. Maximum limit is 100.
- image
Uri String - The URI of a container image in the Container Registry that is to be run on each worker replica.
GoogleCloudAiplatformV1CustomJobSpec, GoogleCloudAiplatformV1CustomJobSpecArgs
- Worker
Pool List<Pulumi.Specs Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Worker Pool Spec> - The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
- Base
Output Pulumi.Directory Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Gcs Destination - The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR =
/model/
* AIP_CHECKPOINT_DIR =/checkpoints/
* AIP_TENSORBOARD_LOG_DIR =/logs/
For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR =//model/
* AIP_CHECKPOINT_DIR =//checkpoints/
* AIP_TENSORBOARD_LOG_DIR =//logs/
- Enable
Dashboard boolAccess - Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to
true
, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - Enable
Web boolAccess - Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to
true
, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - Experiment string
- Optional. The Experiment associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
- Experiment
Run string - Optional. The Experiment Run associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
- Network string
- Optional. The full name of the Compute Engine network to which the Job should be peered. For example,
projects/12345/global/networks/myVPC
. Format is of the formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network. - Protected
Artifact stringLocation Id - The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
- Reserved
Ip List<string>Ranges - Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
- Scheduling
Pulumi.
Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Scheduling - Scheduling options for a CustomJob.
- Service
Account string - Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
- Tensorboard string
- Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format:
projects/{project}/locations/{location}/tensorboards/{tensorboard}
- Worker
Pool []GoogleSpecs Cloud Aiplatform V1Worker Pool Spec - The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
- Base
Output GoogleDirectory Cloud Aiplatform V1Gcs Destination - The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR =
/model/
* AIP_CHECKPOINT_DIR =/checkpoints/
* AIP_TENSORBOARD_LOG_DIR =/logs/
For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR =//model/
* AIP_CHECKPOINT_DIR =//checkpoints/
* AIP_TENSORBOARD_LOG_DIR =//logs/
- Enable
Dashboard boolAccess - Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to
true
, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - Enable
Web boolAccess - Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to
true
, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - Experiment string
- Optional. The Experiment associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
- Experiment
Run string - Optional. The Experiment Run associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
- Network string
- Optional. The full name of the Compute Engine network to which the Job should be peered. For example,
projects/12345/global/networks/myVPC
. Format is of the formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network. - Protected
Artifact stringLocation Id - The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
- Reserved
Ip []stringRanges - Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
- Scheduling
Google
Cloud Aiplatform V1Scheduling - Scheduling options for a CustomJob.
- Service
Account string - Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
- Tensorboard string
- Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format:
projects/{project}/locations/{location}/tensorboards/{tensorboard}
- worker
Pool List<GoogleSpecs Cloud Aiplatform V1Worker Pool Spec> - The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
- base
Output GoogleDirectory Cloud Aiplatform V1Gcs Destination - The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR =
/model/
* AIP_CHECKPOINT_DIR =/checkpoints/
* AIP_TENSORBOARD_LOG_DIR =/logs/
For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR =//model/
* AIP_CHECKPOINT_DIR =//checkpoints/
* AIP_TENSORBOARD_LOG_DIR =//logs/
- enable
Dashboard BooleanAccess - Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to
true
, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - enable
Web BooleanAccess - Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to
true
, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - experiment String
- Optional. The Experiment associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
- experiment
Run String - Optional. The Experiment Run associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
- network String
- Optional. The full name of the Compute Engine network to which the Job should be peered. For example,
projects/12345/global/networks/myVPC
. Format is of the formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network. - protected
Artifact StringLocation Id - The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
- reserved
Ip List<String>Ranges - Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
- scheduling
Google
Cloud Aiplatform V1Scheduling - Scheduling options for a CustomJob.
- service
Account String - Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
- tensorboard String
- Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format:
projects/{project}/locations/{location}/tensorboards/{tensorboard}
- worker
Pool GoogleSpecs Cloud Aiplatform V1Worker Pool Spec[] - The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
- base
Output GoogleDirectory Cloud Aiplatform V1Gcs Destination - The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR =
/model/
* AIP_CHECKPOINT_DIR =/checkpoints/
* AIP_TENSORBOARD_LOG_DIR =/logs/
For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR =//model/
* AIP_CHECKPOINT_DIR =//checkpoints/
* AIP_TENSORBOARD_LOG_DIR =//logs/
- enable
Dashboard booleanAccess - Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to
true
, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - enable
Web booleanAccess - Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to
true
, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - experiment string
- Optional. The Experiment associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
- experiment
Run string - Optional. The Experiment Run associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
- network string
- Optional. The full name of the Compute Engine network to which the Job should be peered. For example,
projects/12345/global/networks/myVPC
. Format is of the formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network. - protected
Artifact stringLocation Id - The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
- reserved
Ip string[]Ranges - Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
- scheduling
Google
Cloud Aiplatform V1Scheduling - Scheduling options for a CustomJob.
- service
Account string - Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
- tensorboard string
- Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format:
projects/{project}/locations/{location}/tensorboards/{tensorboard}
- worker_
pool_ Sequence[Googlespecs Cloud Aiplatform V1Worker Pool Spec] - The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
- base_
output_ Googledirectory Cloud Aiplatform V1Gcs Destination - The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR =
/model/
* AIP_CHECKPOINT_DIR =/checkpoints/
* AIP_TENSORBOARD_LOG_DIR =/logs/
For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR =//model/
* AIP_CHECKPOINT_DIR =//checkpoints/
* AIP_TENSORBOARD_LOG_DIR =//logs/
- enable_
dashboard_ boolaccess - Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to
true
, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - enable_
web_ boolaccess - Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to
true
, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - experiment str
- Optional. The Experiment associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
- experiment_
run str - Optional. The Experiment Run associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
- network str
- Optional. The full name of the Compute Engine network to which the Job should be peered. For example,
projects/12345/global/networks/myVPC
. Format is of the formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network. - protected_
artifact_ strlocation_ id - The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
- reserved_
ip_ Sequence[str]ranges - Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
- scheduling
Google
Cloud Aiplatform V1Scheduling - Scheduling options for a CustomJob.
- service_
account str - Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
- tensorboard str
- Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format:
projects/{project}/locations/{location}/tensorboards/{tensorboard}
- worker
Pool List<Property Map>Specs - The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
- base
Output Property MapDirectory - The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR =
/model/
* AIP_CHECKPOINT_DIR =/checkpoints/
* AIP_TENSORBOARD_LOG_DIR =/logs/
For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR =//model/
* AIP_CHECKPOINT_DIR =//checkpoints/
* AIP_TENSORBOARD_LOG_DIR =//logs/
- enable
Dashboard BooleanAccess - Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to
true
, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - enable
Web BooleanAccess - Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to
true
, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - experiment String
- Optional. The Experiment associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
- experiment
Run String - Optional. The Experiment Run associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
- network String
- Optional. The full name of the Compute Engine network to which the Job should be peered. For example,
projects/12345/global/networks/myVPC
. Format is of the formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network. - protected
Artifact StringLocation Id - The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
- reserved
Ip List<String>Ranges - Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
- scheduling Property Map
- Scheduling options for a CustomJob.
- service
Account String - Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
- tensorboard String
- Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format:
projects/{project}/locations/{location}/tensorboards/{tensorboard}
GoogleCloudAiplatformV1CustomJobSpecResponse, GoogleCloudAiplatformV1CustomJobSpecResponseArgs
- Base
Output Pulumi.Directory Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Gcs Destination Response - The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR =
/model/
* AIP_CHECKPOINT_DIR =/checkpoints/
* AIP_TENSORBOARD_LOG_DIR =/logs/
For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR =//model/
* AIP_CHECKPOINT_DIR =//checkpoints/
* AIP_TENSORBOARD_LOG_DIR =//logs/
- Enable
Dashboard boolAccess - Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to
true
, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - Enable
Web boolAccess - Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to
true
, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - Experiment string
- Optional. The Experiment associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
- Experiment
Run string - Optional. The Experiment Run associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
- Network string
- Optional. The full name of the Compute Engine network to which the Job should be peered. For example,
projects/12345/global/networks/myVPC
. Format is of the formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network. - Protected
Artifact stringLocation Id - The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
- Reserved
Ip List<string>Ranges - Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
- Scheduling
Pulumi.
Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Scheduling Response - Scheduling options for a CustomJob.
- Service
Account string - Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
- Tensorboard string
- Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format:
projects/{project}/locations/{location}/tensorboards/{tensorboard}
- Worker
Pool List<Pulumi.Specs Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Worker Pool Spec Response> - The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
- Base
Output GoogleDirectory Cloud Aiplatform V1Gcs Destination Response - The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR =
/model/
* AIP_CHECKPOINT_DIR =/checkpoints/
* AIP_TENSORBOARD_LOG_DIR =/logs/
For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR =//model/
* AIP_CHECKPOINT_DIR =//checkpoints/
* AIP_TENSORBOARD_LOG_DIR =//logs/
- Enable
Dashboard boolAccess - Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to
true
, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - Enable
Web boolAccess - Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to
true
, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - Experiment string
- Optional. The Experiment associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
- Experiment
Run string - Optional. The Experiment Run associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
- Network string
- Optional. The full name of the Compute Engine network to which the Job should be peered. For example,
projects/12345/global/networks/myVPC
. Format is of the formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network. - Protected
Artifact stringLocation Id - The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
- Reserved
Ip []stringRanges - Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
- Scheduling
Google
Cloud Aiplatform V1Scheduling Response - Scheduling options for a CustomJob.
- Service
Account string - Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
- Tensorboard string
- Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format:
projects/{project}/locations/{location}/tensorboards/{tensorboard}
- Worker
Pool []GoogleSpecs Cloud Aiplatform V1Worker Pool Spec Response - The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
- base
Output GoogleDirectory Cloud Aiplatform V1Gcs Destination Response - The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR =
/model/
* AIP_CHECKPOINT_DIR =/checkpoints/
* AIP_TENSORBOARD_LOG_DIR =/logs/
For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR =//model/
* AIP_CHECKPOINT_DIR =//checkpoints/
* AIP_TENSORBOARD_LOG_DIR =//logs/
- enable
Dashboard BooleanAccess - Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to
true
, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - enable
Web BooleanAccess - Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to
true
, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - experiment String
- Optional. The Experiment associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
- experiment
Run String - Optional. The Experiment Run associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
- network String
- Optional. The full name of the Compute Engine network to which the Job should be peered. For example,
projects/12345/global/networks/myVPC
. Format is of the formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network. - protected
Artifact StringLocation Id - The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
- reserved
Ip List<String>Ranges - Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
- scheduling
Google
Cloud Aiplatform V1Scheduling Response - Scheduling options for a CustomJob.
- service
Account String - Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
- tensorboard String
- Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format:
projects/{project}/locations/{location}/tensorboards/{tensorboard}
- worker
Pool List<GoogleSpecs Cloud Aiplatform V1Worker Pool Spec Response> - The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
- base
Output GoogleDirectory Cloud Aiplatform V1Gcs Destination Response - The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR =
/model/
* AIP_CHECKPOINT_DIR =/checkpoints/
* AIP_TENSORBOARD_LOG_DIR =/logs/
For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR =//model/
* AIP_CHECKPOINT_DIR =//checkpoints/
* AIP_TENSORBOARD_LOG_DIR =//logs/
- enable
Dashboard booleanAccess - Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to
true
, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - enable
Web booleanAccess - Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to
true
, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - experiment string
- Optional. The Experiment associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
- experiment
Run string - Optional. The Experiment Run associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
- network string
- Optional. The full name of the Compute Engine network to which the Job should be peered. For example,
projects/12345/global/networks/myVPC
. Format is of the formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network. - protected
Artifact stringLocation Id - The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
- reserved
Ip string[]Ranges - Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
- scheduling
Google
Cloud Aiplatform V1Scheduling Response - Scheduling options for a CustomJob.
- service
Account string - Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
- tensorboard string
- Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format:
projects/{project}/locations/{location}/tensorboards/{tensorboard}
- worker
Pool GoogleSpecs Cloud Aiplatform V1Worker Pool Spec Response[] - The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
- base_
output_ Googledirectory Cloud Aiplatform V1Gcs Destination Response - The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR =
/model/
* AIP_CHECKPOINT_DIR =/checkpoints/
* AIP_TENSORBOARD_LOG_DIR =/logs/
For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR =//model/
* AIP_CHECKPOINT_DIR =//checkpoints/
* AIP_TENSORBOARD_LOG_DIR =//logs/
- enable_
dashboard_ boolaccess - Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to
true
, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - enable_
web_ boolaccess - Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to
true
, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - experiment str
- Optional. The Experiment associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
- experiment_
run str - Optional. The Experiment Run associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
- network str
- Optional. The full name of the Compute Engine network to which the Job should be peered. For example,
projects/12345/global/networks/myVPC
. Format is of the formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network. - protected_
artifact_ strlocation_ id - The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
- reserved_
ip_ Sequence[str]ranges - Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
- scheduling
Google
Cloud Aiplatform V1Scheduling Response - Scheduling options for a CustomJob.
- service_
account str - Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
- tensorboard str
- Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format:
projects/{project}/locations/{location}/tensorboards/{tensorboard}
- worker_
pool_ Sequence[Googlespecs Cloud Aiplatform V1Worker Pool Spec Response] - The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
- base
Output Property MapDirectory - The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR =
/model/
* AIP_CHECKPOINT_DIR =/checkpoints/
* AIP_TENSORBOARD_LOG_DIR =/logs/
For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR =//model/
* AIP_CHECKPOINT_DIR =//checkpoints/
* AIP_TENSORBOARD_LOG_DIR =//logs/
- enable
Dashboard BooleanAccess - Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to
true
, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - enable
Web BooleanAccess - Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to
true
, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials). - experiment String
- Optional. The Experiment associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
- experiment
Run String - Optional. The Experiment Run associated with this job. Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
- network String
- Optional. The full name of the Compute Engine network to which the Job should be peered. For example,
projects/12345/global/networks/myVPC
. Format is of the formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network. - protected
Artifact StringLocation Id - The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
- reserved
Ip List<String>Ranges - Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
- scheduling Property Map
- Scheduling options for a CustomJob.
- service
Account String - Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.
- tensorboard String
- Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format:
projects/{project}/locations/{location}/tensorboards/{tensorboard}
- worker
Pool List<Property Map>Specs - The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
GoogleCloudAiplatformV1DiskSpec, GoogleCloudAiplatformV1DiskSpecArgs
- Boot
Disk intSize Gb - Size in GB of the boot disk (default is 100GB).
- Boot
Disk stringType - Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
- Boot
Disk intSize Gb - Size in GB of the boot disk (default is 100GB).
- Boot
Disk stringType - Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
- boot
Disk IntegerSize Gb - Size in GB of the boot disk (default is 100GB).
- boot
Disk StringType - Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
- boot
Disk numberSize Gb - Size in GB of the boot disk (default is 100GB).
- boot
Disk stringType - Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
- boot_
disk_ intsize_ gb - Size in GB of the boot disk (default is 100GB).
- boot_
disk_ strtype - Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
- boot
Disk NumberSize Gb - Size in GB of the boot disk (default is 100GB).
- boot
Disk StringType - Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
GoogleCloudAiplatformV1DiskSpecResponse, GoogleCloudAiplatformV1DiskSpecResponseArgs
- Boot
Disk intSize Gb - Size in GB of the boot disk (default is 100GB).
- Boot
Disk stringType - Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
- Boot
Disk intSize Gb - Size in GB of the boot disk (default is 100GB).
- Boot
Disk stringType - Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
- boot
Disk IntegerSize Gb - Size in GB of the boot disk (default is 100GB).
- boot
Disk StringType - Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
- boot
Disk numberSize Gb - Size in GB of the boot disk (default is 100GB).
- boot
Disk stringType - Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
- boot_
disk_ intsize_ gb - Size in GB of the boot disk (default is 100GB).
- boot_
disk_ strtype - Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
- boot
Disk NumberSize Gb - Size in GB of the boot disk (default is 100GB).
- boot
Disk StringType - Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).
GoogleCloudAiplatformV1EncryptionSpec, GoogleCloudAiplatformV1EncryptionSpecArgs
- 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.
GoogleCloudAiplatformV1EncryptionSpecResponse, GoogleCloudAiplatformV1EncryptionSpecResponseArgs
- 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.
GoogleCloudAiplatformV1EnvVar, GoogleCloudAiplatformV1EnvVarArgs
- Name string
- Name of the environment variable. Must be a valid C identifier.
- Value string
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- Name string
- Name of the environment variable. Must be a valid C identifier.
- Value string
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name String
- Name of the environment variable. Must be a valid C identifier.
- value String
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name string
- Name of the environment variable. Must be a valid C identifier.
- value string
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name str
- Name of the environment variable. Must be a valid C identifier.
- value str
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name String
- Name of the environment variable. Must be a valid C identifier.
- value String
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
GoogleCloudAiplatformV1EnvVarResponse, GoogleCloudAiplatformV1EnvVarResponseArgs
- Name string
- Name of the environment variable. Must be a valid C identifier.
- Value string
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- Name string
- Name of the environment variable. Must be a valid C identifier.
- Value string
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name String
- Name of the environment variable. Must be a valid C identifier.
- value String
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name string
- Name of the environment variable. Must be a valid C identifier.
- value string
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name str
- Name of the environment variable. Must be a valid C identifier.
- value str
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
- name String
- Name of the environment variable. Must be a valid C identifier.
- value String
- Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
GoogleCloudAiplatformV1GcsDestination, GoogleCloudAiplatformV1GcsDestinationArgs
- 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.
GoogleCloudAiplatformV1GcsDestinationResponse, GoogleCloudAiplatformV1GcsDestinationResponseArgs
- 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.
GoogleCloudAiplatformV1MachineSpec, GoogleCloudAiplatformV1MachineSpecArgs
- Accelerator
Count int - The number of accelerators to attach to the machine.
- Accelerator
Type Pulumi.Google Native. Aiplatform. V1. Google Cloud Aiplatform V1Machine Spec Accelerator Type - Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
- Machine
Type string - Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is
n1-standard-2
. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. - Tpu
Topology string - Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- Accelerator
Count int - The number of accelerators to attach to the machine.
- Accelerator
Type GoogleCloud Aiplatform V1Machine Spec Accelerator Type - Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
- Machine
Type string - Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is
n1-standard-2
. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. - Tpu
Topology string - Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- accelerator
Count Integer - The number of accelerators to attach to the machine.
- accelerator
Type GoogleCloud Aiplatform V1Machine Spec Accelerator Type - Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
- machine
Type String - Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is
n1-standard-2
. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. - tpu
Topology String - Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- accelerator
Count number - The number of accelerators to attach to the machine.
- accelerator
Type GoogleCloud Aiplatform V1Machine Spec Accelerator Type - Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
- machine
Type string - Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is
n1-standard-2
. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. - tpu
Topology string - Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- accelerator_
count int - The number of accelerators to attach to the machine.
- accelerator_
type GoogleCloud Aiplatform V1Machine Spec Accelerator Type - Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
- machine_
type str - Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is
n1-standard-2
. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. - tpu_
topology str - Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- accelerator
Count Number - The number of accelerators to attach to the machine.
- accelerator
Type "ACCELERATOR_TYPE_UNSPECIFIED" | "NVIDIA_TESLA_K80" | "NVIDIA_TESLA_P100" | "NVIDIA_TESLA_V100" | "NVIDIA_TESLA_P4" | "NVIDIA_TESLA_T4" | "NVIDIA_TESLA_A100" | "NVIDIA_A100_80GB" | "NVIDIA_L4" | "TPU_V2" | "TPU_V3" | "TPU_V4_POD" - Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
- machine
Type String - Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is
n1-standard-2
. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. - tpu
Topology String - Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
GoogleCloudAiplatformV1MachineSpecAcceleratorType, GoogleCloudAiplatformV1MachineSpecAcceleratorTypeArgs
- Accelerator
Type Unspecified - ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
- Nvidia
Tesla K80 - NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
- Nvidia
Tesla P100 - NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
- Nvidia
Tesla V100 - NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
- Nvidia
Tesla P4 - NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
- Nvidia
Tesla T4 - NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
- Nvidia
Tesla A100 - NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
- Nvidia
A10080gb - NVIDIA_A100_80GBNvidia A100 80GB GPU.
- Nvidia
L4 - NVIDIA_L4Nvidia L4 GPU.
- Tpu
V2 - TPU_V2TPU v2.
- Tpu
V3 - TPU_V3TPU v3.
- Tpu
V4Pod - TPU_V4_PODTPU v4.
- Google
Cloud Aiplatform V1Machine Spec Accelerator Type Accelerator Type Unspecified - ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
- Google
Cloud Aiplatform V1Machine Spec Accelerator Type Nvidia Tesla K80 - NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
- Google
Cloud Aiplatform V1Machine Spec Accelerator Type Nvidia Tesla P100 - NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
- Google
Cloud Aiplatform V1Machine Spec Accelerator Type Nvidia Tesla V100 - NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
- Google
Cloud Aiplatform V1Machine Spec Accelerator Type Nvidia Tesla P4 - NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
- Google
Cloud Aiplatform V1Machine Spec Accelerator Type Nvidia Tesla T4 - NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
- Google
Cloud Aiplatform V1Machine Spec Accelerator Type Nvidia Tesla A100 - NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
- Google
Cloud Aiplatform V1Machine Spec Accelerator Type Nvidia A10080gb - NVIDIA_A100_80GBNvidia A100 80GB GPU.
- Google
Cloud Aiplatform V1Machine Spec Accelerator Type Nvidia L4 - NVIDIA_L4Nvidia L4 GPU.
- Google
Cloud Aiplatform V1Machine Spec Accelerator Type Tpu V2 - TPU_V2TPU v2.
- Google
Cloud Aiplatform V1Machine Spec Accelerator Type Tpu V3 - TPU_V3TPU v3.
- Google
Cloud Aiplatform V1Machine Spec Accelerator Type Tpu V4Pod - TPU_V4_PODTPU v4.
- Accelerator
Type Unspecified - ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
- Nvidia
Tesla K80 - NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
- Nvidia
Tesla P100 - NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
- Nvidia
Tesla V100 - NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
- Nvidia
Tesla P4 - NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
- Nvidia
Tesla T4 - NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
- Nvidia
Tesla A100 - NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
- Nvidia
A10080gb - NVIDIA_A100_80GBNvidia A100 80GB GPU.
- Nvidia
L4 - NVIDIA_L4Nvidia L4 GPU.
- Tpu
V2 - TPU_V2TPU v2.
- Tpu
V3 - TPU_V3TPU v3.
- Tpu
V4Pod - TPU_V4_PODTPU v4.
- Accelerator
Type Unspecified - ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
- Nvidia
Tesla K80 - NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
- Nvidia
Tesla P100 - NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
- Nvidia
Tesla V100 - NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
- Nvidia
Tesla P4 - NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
- Nvidia
Tesla T4 - NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
- Nvidia
Tesla A100 - NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
- Nvidia
A10080gb - NVIDIA_A100_80GBNvidia A100 80GB GPU.
- Nvidia
L4 - NVIDIA_L4Nvidia L4 GPU.
- Tpu
V2 - TPU_V2TPU v2.
- Tpu
V3 - TPU_V3TPU v3.
- Tpu
V4Pod - TPU_V4_PODTPU v4.
- ACCELERATOR_TYPE_UNSPECIFIED
- ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
- NVIDIA_TESLA_K80
- NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
- NVIDIA_TESLA_P100
- NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
- NVIDIA_TESLA_V100
- NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
- NVIDIA_TESLA_P4
- NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
- NVIDIA_TESLA_T4
- NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
- NVIDIA_TESLA_A100
- NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
- NVIDIA_A10080GB
- NVIDIA_A100_80GBNvidia A100 80GB GPU.
- NVIDIA_L4
- NVIDIA_L4Nvidia L4 GPU.
- TPU_V2
- TPU_V2TPU v2.
- TPU_V3
- TPU_V3TPU v3.
- TPU_V4_POD
- TPU_V4_PODTPU v4.
- "ACCELERATOR_TYPE_UNSPECIFIED"
- ACCELERATOR_TYPE_UNSPECIFIEDUnspecified accelerator type, which means no accelerator.
- "NVIDIA_TESLA_K80"
- NVIDIA_TESLA_K80Nvidia Tesla K80 GPU.
- "NVIDIA_TESLA_P100"
- NVIDIA_TESLA_P100Nvidia Tesla P100 GPU.
- "NVIDIA_TESLA_V100"
- NVIDIA_TESLA_V100Nvidia Tesla V100 GPU.
- "NVIDIA_TESLA_P4"
- NVIDIA_TESLA_P4Nvidia Tesla P4 GPU.
- "NVIDIA_TESLA_T4"
- NVIDIA_TESLA_T4Nvidia Tesla T4 GPU.
- "NVIDIA_TESLA_A100"
- NVIDIA_TESLA_A100Nvidia Tesla A100 GPU.
- "NVIDIA_A100_80GB"
- NVIDIA_A100_80GBNvidia A100 80GB GPU.
- "NVIDIA_L4"
- NVIDIA_L4Nvidia L4 GPU.
- "TPU_V2"
- TPU_V2TPU v2.
- "TPU_V3"
- TPU_V3TPU v3.
- "TPU_V4_POD"
- TPU_V4_PODTPU v4.
GoogleCloudAiplatformV1MachineSpecResponse, GoogleCloudAiplatformV1MachineSpecResponseArgs
- Accelerator
Count int - The number of accelerators to attach to the machine.
- Accelerator
Type string - Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
- Machine
Type string - Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is
n1-standard-2
. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. - Tpu
Topology string - Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- Accelerator
Count int - The number of accelerators to attach to the machine.
- Accelerator
Type string - Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
- Machine
Type string - Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is
n1-standard-2
. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. - Tpu
Topology string - Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- accelerator
Count Integer - The number of accelerators to attach to the machine.
- accelerator
Type String - Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
- machine
Type String - Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is
n1-standard-2
. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. - tpu
Topology String - Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- accelerator
Count number - The number of accelerators to attach to the machine.
- accelerator
Type string - Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
- machine
Type string - Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is
n1-standard-2
. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. - tpu
Topology string - Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- accelerator_
count int - The number of accelerators to attach to the machine.
- accelerator_
type str - Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
- machine_
type str - Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is
n1-standard-2
. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. - tpu_
topology str - Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- accelerator
Count Number - The number of accelerators to attach to the machine.
- accelerator
Type String - Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
- machine
Type String - Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is
n1-standard-2
. For BatchPredictionJob or as part of WorkerPoolSpec this field is required. - tpu
Topology String - Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
GoogleCloudAiplatformV1MeasurementMetricResponse, GoogleCloudAiplatformV1MeasurementMetricResponseArgs
GoogleCloudAiplatformV1MeasurementResponse, GoogleCloudAiplatformV1MeasurementResponseArgs
- Elapsed
Duration string - Time that the Trial has been running at the point of this Measurement.
- Metrics
List<Pulumi.
Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Measurement Metric Response> - A list of metrics got by evaluating the objective functions using suggested Parameter values.
- Step
Count string - The number of steps the machine learning model has been trained for. Must be non-negative.
- Elapsed
Duration string - Time that the Trial has been running at the point of this Measurement.
- Metrics
[]Google
Cloud Aiplatform V1Measurement Metric Response - A list of metrics got by evaluating the objective functions using suggested Parameter values.
- Step
Count string - The number of steps the machine learning model has been trained for. Must be non-negative.
- elapsed
Duration String - Time that the Trial has been running at the point of this Measurement.
- metrics
List<Google
Cloud Aiplatform V1Measurement Metric Response> - A list of metrics got by evaluating the objective functions using suggested Parameter values.
- step
Count String - The number of steps the machine learning model has been trained for. Must be non-negative.
- elapsed
Duration string - Time that the Trial has been running at the point of this Measurement.
- metrics
Google
Cloud Aiplatform V1Measurement Metric Response[] - A list of metrics got by evaluating the objective functions using suggested Parameter values.
- step
Count string - The number of steps the machine learning model has been trained for. Must be non-negative.
- elapsed_
duration str - Time that the Trial has been running at the point of this Measurement.
- metrics
Sequence[Google
Cloud Aiplatform V1Measurement Metric Response] - A list of metrics got by evaluating the objective functions using suggested Parameter values.
- step_
count str - The number of steps the machine learning model has been trained for. Must be non-negative.
- elapsed
Duration String - Time that the Trial has been running at the point of this Measurement.
- metrics List<Property Map>
- A list of metrics got by evaluating the objective functions using suggested Parameter values.
- step
Count String - The number of steps the machine learning model has been trained for. Must be non-negative.
GoogleCloudAiplatformV1NfsMount, GoogleCloudAiplatformV1NfsMountArgs
- Mount
Point string - Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
- Path string
- Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of
server:path
- Server string
- IP address of the NFS server.
- Mount
Point string - Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
- Path string
- Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of
server:path
- Server string
- IP address of the NFS server.
- mount
Point String - Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
- path String
- Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of
server:path
- server String
- IP address of the NFS server.
- mount
Point string - Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
- path string
- Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of
server:path
- server string
- IP address of the NFS server.
- mount_
point str - Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
- path str
- Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of
server:path
- server str
- IP address of the NFS server.
- mount
Point String - Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
- path String
- Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of
server:path
- server String
- IP address of the NFS server.
GoogleCloudAiplatformV1NfsMountResponse, GoogleCloudAiplatformV1NfsMountResponseArgs
- Mount
Point string - Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
- Path string
- Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of
server:path
- Server string
- IP address of the NFS server.
- Mount
Point string - Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
- Path string
- Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of
server:path
- Server string
- IP address of the NFS server.
- mount
Point String - Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
- path String
- Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of
server:path
- server String
- IP address of the NFS server.
- mount
Point string - Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
- path string
- Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of
server:path
- server string
- IP address of the NFS server.
- mount_
point str - Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
- path str
- Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of
server:path
- server str
- IP address of the NFS server.
- mount
Point String - Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
- path String
- Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of
server:path
- server String
- IP address of the NFS server.
GoogleCloudAiplatformV1PythonPackageSpec, GoogleCloudAiplatformV1PythonPackageSpecArgs
- Executor
Image stringUri - The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
- Package
Uris List<string> - The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
- Python
Module string - The Python module name to run after installing the packages.
- Args List<string>
- Command line arguments to be passed to the Python task.
- Env
List<Pulumi.
Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Env Var> - Environment variables to be passed to the python module. Maximum limit is 100.
- Executor
Image stringUri - The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
- Package
Uris []string - The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
- Python
Module string - The Python module name to run after installing the packages.
- Args []string
- Command line arguments to be passed to the Python task.
- Env
[]Google
Cloud Aiplatform V1Env Var - Environment variables to be passed to the python module. Maximum limit is 100.
- executor
Image StringUri - The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
- package
Uris List<String> - The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
- python
Module String - The Python module name to run after installing the packages.
- args List<String>
- Command line arguments to be passed to the Python task.
- env
List<Google
Cloud Aiplatform V1Env Var> - Environment variables to be passed to the python module. Maximum limit is 100.
- executor
Image stringUri - The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
- package
Uris string[] - The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
- python
Module string - The Python module name to run after installing the packages.
- args string[]
- Command line arguments to be passed to the Python task.
- env
Google
Cloud Aiplatform V1Env Var[] - Environment variables to be passed to the python module. Maximum limit is 100.
- executor_
image_ struri - The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
- package_
uris Sequence[str] - The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
- python_
module str - The Python module name to run after installing the packages.
- args Sequence[str]
- Command line arguments to be passed to the Python task.
- env
Sequence[Google
Cloud Aiplatform V1Env Var] - Environment variables to be passed to the python module. Maximum limit is 100.
- executor
Image StringUri - The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
- package
Uris List<String> - The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
- python
Module String - The Python module name to run after installing the packages.
- args List<String>
- Command line arguments to be passed to the Python task.
- env List<Property Map>
- Environment variables to be passed to the python module. Maximum limit is 100.
GoogleCloudAiplatformV1PythonPackageSpecResponse, GoogleCloudAiplatformV1PythonPackageSpecResponseArgs
- Args List<string>
- Command line arguments to be passed to the Python task.
- Env
List<Pulumi.
Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Env Var Response> - Environment variables to be passed to the python module. Maximum limit is 100.
- Executor
Image stringUri - The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
- Package
Uris List<string> - The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
- Python
Module string - The Python module name to run after installing the packages.
- Args []string
- Command line arguments to be passed to the Python task.
- Env
[]Google
Cloud Aiplatform V1Env Var Response - Environment variables to be passed to the python module. Maximum limit is 100.
- Executor
Image stringUri - The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
- Package
Uris []string - The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
- Python
Module string - The Python module name to run after installing the packages.
- args List<String>
- Command line arguments to be passed to the Python task.
- env
List<Google
Cloud Aiplatform V1Env Var Response> - Environment variables to be passed to the python module. Maximum limit is 100.
- executor
Image StringUri - The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
- package
Uris List<String> - The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
- python
Module String - The Python module name to run after installing the packages.
- args string[]
- Command line arguments to be passed to the Python task.
- env
Google
Cloud Aiplatform V1Env Var Response[] - Environment variables to be passed to the python module. Maximum limit is 100.
- executor
Image stringUri - The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
- package
Uris string[] - The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
- python
Module string - The Python module name to run after installing the packages.
- args Sequence[str]
- Command line arguments to be passed to the Python task.
- env
Sequence[Google
Cloud Aiplatform V1Env Var Response] - Environment variables to be passed to the python module. Maximum limit is 100.
- executor_
image_ struri - The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
- package_
uris Sequence[str] - The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
- python_
module str - The Python module name to run after installing the packages.
- args List<String>
- Command line arguments to be passed to the Python task.
- env List<Property Map>
- Environment variables to be passed to the python module. Maximum limit is 100.
- executor
Image StringUri - The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.
- package
Uris List<String> - The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
- python
Module String - The Python module name to run after installing the packages.
GoogleCloudAiplatformV1Scheduling, GoogleCloudAiplatformV1SchedulingArgs
- Disable
Retries bool - Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides
Scheduling.restart_job_on_worker_restart
to false. - Restart
Job boolOn Worker Restart - Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
- Timeout string
- The maximum job running time. The default is 7 days.
- Disable
Retries bool - Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides
Scheduling.restart_job_on_worker_restart
to false. - Restart
Job boolOn Worker Restart - Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
- Timeout string
- The maximum job running time. The default is 7 days.
- disable
Retries Boolean - Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides
Scheduling.restart_job_on_worker_restart
to false. - restart
Job BooleanOn Worker Restart - Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
- timeout String
- The maximum job running time. The default is 7 days.
- disable
Retries boolean - Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides
Scheduling.restart_job_on_worker_restart
to false. - restart
Job booleanOn Worker Restart - Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
- timeout string
- The maximum job running time. The default is 7 days.
- disable_
retries bool - Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides
Scheduling.restart_job_on_worker_restart
to false. - restart_
job_ boolon_ worker_ restart - Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
- timeout str
- The maximum job running time. The default is 7 days.
- disable
Retries Boolean - Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides
Scheduling.restart_job_on_worker_restart
to false. - restart
Job BooleanOn Worker Restart - Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
- timeout String
- The maximum job running time. The default is 7 days.
GoogleCloudAiplatformV1SchedulingResponse, GoogleCloudAiplatformV1SchedulingResponseArgs
- Disable
Retries bool - Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides
Scheduling.restart_job_on_worker_restart
to false. - Restart
Job boolOn Worker Restart - Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
- Timeout string
- The maximum job running time. The default is 7 days.
- Disable
Retries bool - Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides
Scheduling.restart_job_on_worker_restart
to false. - Restart
Job boolOn Worker Restart - Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
- Timeout string
- The maximum job running time. The default is 7 days.
- disable
Retries Boolean - Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides
Scheduling.restart_job_on_worker_restart
to false. - restart
Job BooleanOn Worker Restart - Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
- timeout String
- The maximum job running time. The default is 7 days.
- disable
Retries boolean - Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides
Scheduling.restart_job_on_worker_restart
to false. - restart
Job booleanOn Worker Restart - Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
- timeout string
- The maximum job running time. The default is 7 days.
- disable_
retries bool - Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides
Scheduling.restart_job_on_worker_restart
to false. - restart_
job_ boolon_ worker_ restart - Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
- timeout str
- The maximum job running time. The default is 7 days.
- disable
Retries Boolean - Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides
Scheduling.restart_job_on_worker_restart
to false. - restart
Job BooleanOn Worker Restart - Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
- timeout String
- The maximum job running time. The default is 7 days.
GoogleCloudAiplatformV1StudySpec, GoogleCloudAiplatformV1StudySpecArgs
- Metrics
List<Pulumi.
Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Metric Spec> - Metric specs for the Study.
- Parameters
List<Pulumi.
Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec> - The set of parameters to tune.
- Algorithm
Pulumi.
Google Native. Aiplatform. V1. Google Cloud Aiplatform V1Study Spec Algorithm - The search algorithm specified for the Study.
- Convex
Automated Pulumi.Stopping Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Convex Automated Stopping Spec - The automated early stopping spec using convex stopping rule.
- Decay
Curve Pulumi.Stopping Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Decay Curve Automated Stopping Spec - The automated early stopping spec using decay curve rule.
- Measurement
Selection Pulumi.Type Google Native. Aiplatform. V1. Google Cloud Aiplatform V1Study Spec Measurement Selection Type - Describe which measurement selection type will be used
- Median
Automated Pulumi.Stopping Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Median Automated Stopping Spec - The automated early stopping spec using median rule.
- Observation
Noise Pulumi.Google Native. Aiplatform. V1. Google Cloud Aiplatform V1Study Spec Observation Noise - The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- Study
Stopping Pulumi.Config Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Study Stopping Config - Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
- Metrics
[]Google
Cloud Aiplatform V1Study Spec Metric Spec - Metric specs for the Study.
- Parameters
[]Google
Cloud Aiplatform V1Study Spec Parameter Spec - The set of parameters to tune.
- Algorithm
Google
Cloud Aiplatform V1Study Spec Algorithm - The search algorithm specified for the Study.
- Convex
Automated GoogleStopping Spec Cloud Aiplatform V1Study Spec Convex Automated Stopping Spec - The automated early stopping spec using convex stopping rule.
- Decay
Curve GoogleStopping Spec Cloud Aiplatform V1Study Spec Decay Curve Automated Stopping Spec - The automated early stopping spec using decay curve rule.
- Measurement
Selection GoogleType Cloud Aiplatform V1Study Spec Measurement Selection Type - Describe which measurement selection type will be used
- Median
Automated GoogleStopping Spec Cloud Aiplatform V1Study Spec Median Automated Stopping Spec - The automated early stopping spec using median rule.
- Observation
Noise GoogleCloud Aiplatform V1Study Spec Observation Noise - The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- Study
Stopping GoogleConfig Cloud Aiplatform V1Study Spec Study Stopping Config - Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
- metrics
List<Google
Cloud Aiplatform V1Study Spec Metric Spec> - Metric specs for the Study.
- parameters
List<Google
Cloud Aiplatform V1Study Spec Parameter Spec> - The set of parameters to tune.
- algorithm
Google
Cloud Aiplatform V1Study Spec Algorithm - The search algorithm specified for the Study.
- convex
Automated GoogleStopping Spec Cloud Aiplatform V1Study Spec Convex Automated Stopping Spec - The automated early stopping spec using convex stopping rule.
- decay
Curve GoogleStopping Spec Cloud Aiplatform V1Study Spec Decay Curve Automated Stopping Spec - The automated early stopping spec using decay curve rule.
- measurement
Selection GoogleType Cloud Aiplatform V1Study Spec Measurement Selection Type - Describe which measurement selection type will be used
- median
Automated GoogleStopping Spec Cloud Aiplatform V1Study Spec Median Automated Stopping Spec - The automated early stopping spec using median rule.
- observation
Noise GoogleCloud Aiplatform V1Study Spec Observation Noise - The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- study
Stopping GoogleConfig Cloud Aiplatform V1Study Spec Study Stopping Config - Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
- metrics
Google
Cloud Aiplatform V1Study Spec Metric Spec[] - Metric specs for the Study.
- parameters
Google
Cloud Aiplatform V1Study Spec Parameter Spec[] - The set of parameters to tune.
- algorithm
Google
Cloud Aiplatform V1Study Spec Algorithm - The search algorithm specified for the Study.
- convex
Automated GoogleStopping Spec Cloud Aiplatform V1Study Spec Convex Automated Stopping Spec - The automated early stopping spec using convex stopping rule.
- decay
Curve GoogleStopping Spec Cloud Aiplatform V1Study Spec Decay Curve Automated Stopping Spec - The automated early stopping spec using decay curve rule.
- measurement
Selection GoogleType Cloud Aiplatform V1Study Spec Measurement Selection Type - Describe which measurement selection type will be used
- median
Automated GoogleStopping Spec Cloud Aiplatform V1Study Spec Median Automated Stopping Spec - The automated early stopping spec using median rule.
- observation
Noise GoogleCloud Aiplatform V1Study Spec Observation Noise - The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- study
Stopping GoogleConfig Cloud Aiplatform V1Study Spec Study Stopping Config - Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
- metrics
Sequence[Google
Cloud Aiplatform V1Study Spec Metric Spec] - Metric specs for the Study.
- parameters
Sequence[Google
Cloud Aiplatform V1Study Spec Parameter Spec] - The set of parameters to tune.
- algorithm
Google
Cloud Aiplatform V1Study Spec Algorithm - The search algorithm specified for the Study.
- convex_
automated_ Googlestopping_ spec Cloud Aiplatform V1Study Spec Convex Automated Stopping Spec - The automated early stopping spec using convex stopping rule.
- decay_
curve_ Googlestopping_ spec Cloud Aiplatform V1Study Spec Decay Curve Automated Stopping Spec - The automated early stopping spec using decay curve rule.
- measurement_
selection_ Googletype Cloud Aiplatform V1Study Spec Measurement Selection Type - Describe which measurement selection type will be used
- median_
automated_ Googlestopping_ spec Cloud Aiplatform V1Study Spec Median Automated Stopping Spec - The automated early stopping spec using median rule.
- observation_
noise GoogleCloud Aiplatform V1Study Spec Observation Noise - The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- study_
stopping_ Googleconfig Cloud Aiplatform V1Study Spec Study Stopping Config - Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
- metrics List<Property Map>
- Metric specs for the Study.
- parameters List<Property Map>
- The set of parameters to tune.
- algorithm "ALGORITHM_UNSPECIFIED" | "GRID_SEARCH" | "RANDOM_SEARCH"
- The search algorithm specified for the Study.
- convex
Automated Property MapStopping Spec - The automated early stopping spec using convex stopping rule.
- decay
Curve Property MapStopping Spec - The automated early stopping spec using decay curve rule.
- measurement
Selection "MEASUREMENT_SELECTION_TYPE_UNSPECIFIED" | "LAST_MEASUREMENT" | "BEST_MEASUREMENT"Type - Describe which measurement selection type will be used
- median
Automated Property MapStopping Spec - The automated early stopping spec using median rule.
- observation
Noise "OBSERVATION_NOISE_UNSPECIFIED" | "LOW" | "HIGH" - The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- study
Stopping Property MapConfig - Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
GoogleCloudAiplatformV1StudySpecAlgorithm, GoogleCloudAiplatformV1StudySpecAlgorithmArgs
- Algorithm
Unspecified - ALGORITHM_UNSPECIFIEDThe default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.
- Grid
Search - GRID_SEARCHSimple grid search within the feasible space. To use grid search, all parameters must be
INTEGER
,CATEGORICAL
, orDISCRETE
. - Random
Search - RANDOM_SEARCHSimple random search within the feasible space.
- Google
Cloud Aiplatform V1Study Spec Algorithm Algorithm Unspecified - ALGORITHM_UNSPECIFIEDThe default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.
- Google
Cloud Aiplatform V1Study Spec Algorithm Grid Search - GRID_SEARCHSimple grid search within the feasible space. To use grid search, all parameters must be
INTEGER
,CATEGORICAL
, orDISCRETE
. - Google
Cloud Aiplatform V1Study Spec Algorithm Random Search - RANDOM_SEARCHSimple random search within the feasible space.
- Algorithm
Unspecified - ALGORITHM_UNSPECIFIEDThe default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.
- Grid
Search - GRID_SEARCHSimple grid search within the feasible space. To use grid search, all parameters must be
INTEGER
,CATEGORICAL
, orDISCRETE
. - Random
Search - RANDOM_SEARCHSimple random search within the feasible space.
- Algorithm
Unspecified - ALGORITHM_UNSPECIFIEDThe default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.
- Grid
Search - GRID_SEARCHSimple grid search within the feasible space. To use grid search, all parameters must be
INTEGER
,CATEGORICAL
, orDISCRETE
. - Random
Search - RANDOM_SEARCHSimple random search within the feasible space.
- ALGORITHM_UNSPECIFIED
- ALGORITHM_UNSPECIFIEDThe default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.
- GRID_SEARCH
- GRID_SEARCHSimple grid search within the feasible space. To use grid search, all parameters must be
INTEGER
,CATEGORICAL
, orDISCRETE
. - RANDOM_SEARCH
- RANDOM_SEARCHSimple random search within the feasible space.
- "ALGORITHM_UNSPECIFIED"
- ALGORITHM_UNSPECIFIEDThe default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.
- "GRID_SEARCH"
- GRID_SEARCHSimple grid search within the feasible space. To use grid search, all parameters must be
INTEGER
,CATEGORICAL
, orDISCRETE
. - "RANDOM_SEARCH"
- RANDOM_SEARCHSimple random search within the feasible space.
GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpec, GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpecArgs
- Learning
Rate stringParameter Name - The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
- Max
Step stringCount - Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
- Min
Measurement stringCount - The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
- Min
Step stringCount - Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
- Update
All boolStopped Trials - ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their
final_measurement
. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future. - Use
Elapsed boolDuration - This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
- Learning
Rate stringParameter Name - The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
- Max
Step stringCount - Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
- Min
Measurement stringCount - The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
- Min
Step stringCount - Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
- Update
All boolStopped Trials - ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their
final_measurement
. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future. - Use
Elapsed boolDuration - This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
- learning
Rate StringParameter Name - The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
- max
Step StringCount - Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
- min
Measurement StringCount - The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
- min
Step StringCount - Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
- update
All BooleanStopped Trials - ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their
final_measurement
. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future. - use
Elapsed BooleanDuration - This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
- learning
Rate stringParameter Name - The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
- max
Step stringCount - Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
- min
Measurement stringCount - The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
- min
Step stringCount - Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
- update
All booleanStopped Trials - ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their
final_measurement
. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future. - use
Elapsed booleanDuration - This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
- learning_
rate_ strparameter_ name - The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
- max_
step_ strcount - Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
- min_
measurement_ strcount - The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
- min_
step_ strcount - Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
- update_
all_ boolstopped_ trials - ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their
final_measurement
. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future. - use_
elapsed_ boolduration - This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
- learning
Rate StringParameter Name - The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
- max
Step StringCount - Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
- min
Measurement StringCount - The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
- min
Step StringCount - Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
- update
All BooleanStopped Trials - ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their
final_measurement
. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future. - use
Elapsed BooleanDuration - This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpecResponse, GoogleCloudAiplatformV1StudySpecConvexAutomatedStoppingSpecResponseArgs
- Learning
Rate stringParameter Name - The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
- Max
Step stringCount - Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
- Min
Measurement stringCount - The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
- Min
Step stringCount - Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
- Update
All boolStopped Trials - ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their
final_measurement
. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future. - Use
Elapsed boolDuration - This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
- Learning
Rate stringParameter Name - The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
- Max
Step stringCount - Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
- Min
Measurement stringCount - The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
- Min
Step stringCount - Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
- Update
All boolStopped Trials - ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their
final_measurement
. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future. - Use
Elapsed boolDuration - This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
- learning
Rate StringParameter Name - The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
- max
Step StringCount - Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
- min
Measurement StringCount - The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
- min
Step StringCount - Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
- update
All BooleanStopped Trials - ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their
final_measurement
. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future. - use
Elapsed BooleanDuration - This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
- learning
Rate stringParameter Name - The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
- max
Step stringCount - Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
- min
Measurement stringCount - The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
- min
Step stringCount - Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
- update
All booleanStopped Trials - ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their
final_measurement
. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future. - use
Elapsed booleanDuration - This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
- learning_
rate_ strparameter_ name - The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
- max_
step_ strcount - Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
- min_
measurement_ strcount - The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
- min_
step_ strcount - Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
- update_
all_ boolstopped_ trials - ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their
final_measurement
. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future. - use_
elapsed_ boolduration - This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
- learning
Rate StringParameter Name - The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
- max
Step StringCount - Steps used in predicting the final objective for early stopped trials. In general, it's set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
- min
Measurement StringCount - The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
- min
Step StringCount - Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won't be considered for early stopping. It's ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
- update
All BooleanStopped Trials - ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their
final_measurement
. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future. - use
Elapsed BooleanDuration - This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpec, GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpecArgs
- Use
Elapsed boolDuration - True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
- Use
Elapsed boolDuration - True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
- use
Elapsed BooleanDuration - True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
- use
Elapsed booleanDuration - True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
- use_
elapsed_ boolduration - True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
- use
Elapsed BooleanDuration - True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpecResponse, GoogleCloudAiplatformV1StudySpecDecayCurveAutomatedStoppingSpecResponseArgs
- Use
Elapsed boolDuration - True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
- Use
Elapsed boolDuration - True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
- use
Elapsed BooleanDuration - True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
- use
Elapsed booleanDuration - True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
- use_
elapsed_ boolduration - True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
- use
Elapsed BooleanDuration - True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
GoogleCloudAiplatformV1StudySpecMeasurementSelectionType, GoogleCloudAiplatformV1StudySpecMeasurementSelectionTypeArgs
- Measurement
Selection Type Unspecified - MEASUREMENT_SELECTION_TYPE_UNSPECIFIEDWill be treated as LAST_MEASUREMENT.
- Last
Measurement - LAST_MEASUREMENTUse the last measurement reported.
- Best
Measurement - BEST_MEASUREMENTUse the best measurement reported.
- Google
Cloud Aiplatform V1Study Spec Measurement Selection Type Measurement Selection Type Unspecified - MEASUREMENT_SELECTION_TYPE_UNSPECIFIEDWill be treated as LAST_MEASUREMENT.
- Google
Cloud Aiplatform V1Study Spec Measurement Selection Type Last Measurement - LAST_MEASUREMENTUse the last measurement reported.
- Google
Cloud Aiplatform V1Study Spec Measurement Selection Type Best Measurement - BEST_MEASUREMENTUse the best measurement reported.
- Measurement
Selection Type Unspecified - MEASUREMENT_SELECTION_TYPE_UNSPECIFIEDWill be treated as LAST_MEASUREMENT.
- Last
Measurement - LAST_MEASUREMENTUse the last measurement reported.
- Best
Measurement - BEST_MEASUREMENTUse the best measurement reported.
- Measurement
Selection Type Unspecified - MEASUREMENT_SELECTION_TYPE_UNSPECIFIEDWill be treated as LAST_MEASUREMENT.
- Last
Measurement - LAST_MEASUREMENTUse the last measurement reported.
- Best
Measurement - BEST_MEASUREMENTUse the best measurement reported.
- MEASUREMENT_SELECTION_TYPE_UNSPECIFIED
- MEASUREMENT_SELECTION_TYPE_UNSPECIFIEDWill be treated as LAST_MEASUREMENT.
- LAST_MEASUREMENT
- LAST_MEASUREMENTUse the last measurement reported.
- BEST_MEASUREMENT
- BEST_MEASUREMENTUse the best measurement reported.
- "MEASUREMENT_SELECTION_TYPE_UNSPECIFIED"
- MEASUREMENT_SELECTION_TYPE_UNSPECIFIEDWill be treated as LAST_MEASUREMENT.
- "LAST_MEASUREMENT"
- LAST_MEASUREMENTUse the last measurement reported.
- "BEST_MEASUREMENT"
- BEST_MEASUREMENTUse the best measurement reported.
GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpec, GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpecArgs
- Use
Elapsed boolDuration - True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
- Use
Elapsed boolDuration - True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
- use
Elapsed BooleanDuration - True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
- use
Elapsed booleanDuration - True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
- use_
elapsed_ boolduration - True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
- use
Elapsed BooleanDuration - True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpecResponse, GoogleCloudAiplatformV1StudySpecMedianAutomatedStoppingSpecResponseArgs
- Use
Elapsed boolDuration - True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
- Use
Elapsed boolDuration - True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
- use
Elapsed BooleanDuration - True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
- use
Elapsed booleanDuration - True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
- use_
elapsed_ boolduration - True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
- use
Elapsed BooleanDuration - True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
GoogleCloudAiplatformV1StudySpecMetricSpec, GoogleCloudAiplatformV1StudySpecMetricSpecArgs
- Goal
Pulumi.
Google Native. Aiplatform. V1. Google Cloud Aiplatform V1Study Spec Metric Spec Goal - The optimization goal of the metric.
- Metric
Id string - The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
- Safety
Config Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Metric Spec Safety Metric Config - Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
- Goal
Google
Cloud Aiplatform V1Study Spec Metric Spec Goal - The optimization goal of the metric.
- Metric
Id string - The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
- Safety
Config GoogleCloud Aiplatform V1Study Spec Metric Spec Safety Metric Config - Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
- goal
Google
Cloud Aiplatform V1Study Spec Metric Spec Goal - The optimization goal of the metric.
- metric
Id String - The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
- safety
Config GoogleCloud Aiplatform V1Study Spec Metric Spec Safety Metric Config - Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
- goal
Google
Cloud Aiplatform V1Study Spec Metric Spec Goal - The optimization goal of the metric.
- metric
Id string - The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
- safety
Config GoogleCloud Aiplatform V1Study Spec Metric Spec Safety Metric Config - Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
- goal
Google
Cloud Aiplatform V1Study Spec Metric Spec Goal - The optimization goal of the metric.
- metric_
id str - The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
- safety_
config GoogleCloud Aiplatform V1Study Spec Metric Spec Safety Metric Config - Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
- goal "GOAL_TYPE_UNSPECIFIED" | "MAXIMIZE" | "MINIMIZE"
- The optimization goal of the metric.
- metric
Id String - The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
- safety
Config Property Map - Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
GoogleCloudAiplatformV1StudySpecMetricSpecGoal, GoogleCloudAiplatformV1StudySpecMetricSpecGoalArgs
- Goal
Type Unspecified - GOAL_TYPE_UNSPECIFIEDGoal Type will default to maximize.
- Maximize
- MAXIMIZEMaximize the goal metric.
- Minimize
- MINIMIZEMinimize the goal metric.
- Google
Cloud Aiplatform V1Study Spec Metric Spec Goal Goal Type Unspecified - GOAL_TYPE_UNSPECIFIEDGoal Type will default to maximize.
- Google
Cloud Aiplatform V1Study Spec Metric Spec Goal Maximize - MAXIMIZEMaximize the goal metric.
- Google
Cloud Aiplatform V1Study Spec Metric Spec Goal Minimize - MINIMIZEMinimize the goal metric.
- Goal
Type Unspecified - GOAL_TYPE_UNSPECIFIEDGoal Type will default to maximize.
- Maximize
- MAXIMIZEMaximize the goal metric.
- Minimize
- MINIMIZEMinimize the goal metric.
- Goal
Type Unspecified - GOAL_TYPE_UNSPECIFIEDGoal Type will default to maximize.
- Maximize
- MAXIMIZEMaximize the goal metric.
- Minimize
- MINIMIZEMinimize the goal metric.
- GOAL_TYPE_UNSPECIFIED
- GOAL_TYPE_UNSPECIFIEDGoal Type will default to maximize.
- MAXIMIZE
- MAXIMIZEMaximize the goal metric.
- MINIMIZE
- MINIMIZEMinimize the goal metric.
- "GOAL_TYPE_UNSPECIFIED"
- GOAL_TYPE_UNSPECIFIEDGoal Type will default to maximize.
- "MAXIMIZE"
- MAXIMIZEMaximize the goal metric.
- "MINIMIZE"
- MINIMIZEMinimize the goal metric.
GoogleCloudAiplatformV1StudySpecMetricSpecResponse, GoogleCloudAiplatformV1StudySpecMetricSpecResponseArgs
- Goal string
- The optimization goal of the metric.
- Metric
Id string - The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
- Safety
Config Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Metric Spec Safety Metric Config Response - Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
- Goal string
- The optimization goal of the metric.
- Metric
Id string - The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
- Safety
Config GoogleCloud Aiplatform V1Study Spec Metric Spec Safety Metric Config Response - Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
- goal String
- The optimization goal of the metric.
- metric
Id String - The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
- safety
Config GoogleCloud Aiplatform V1Study Spec Metric Spec Safety Metric Config Response - Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
- goal string
- The optimization goal of the metric.
- metric
Id string - The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
- safety
Config GoogleCloud Aiplatform V1Study Spec Metric Spec Safety Metric Config Response - Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
- goal str
- The optimization goal of the metric.
- metric_
id str - The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
- safety_
config GoogleCloud Aiplatform V1Study Spec Metric Spec Safety Metric Config Response - Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
- goal String
- The optimization goal of the metric.
- metric
Id String - The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
- safety
Config Property Map - Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfig, GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfigArgs
- Desired
Min doubleSafe Trials Fraction - Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
- Safety
Threshold double - Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
- Desired
Min float64Safe Trials Fraction - Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
- Safety
Threshold float64 - Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
- desired
Min DoubleSafe Trials Fraction - Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
- safety
Threshold Double - Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
- desired
Min numberSafe Trials Fraction - Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
- safety
Threshold number - Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
- desired_
min_ floatsafe_ trials_ fraction - Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
- safety_
threshold float - Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
- desired
Min NumberSafe Trials Fraction - Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
- safety
Threshold Number - Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfigResponse, GoogleCloudAiplatformV1StudySpecMetricSpecSafetyMetricConfigResponseArgs
- Desired
Min doubleSafe Trials Fraction - Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
- Safety
Threshold double - Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
- Desired
Min float64Safe Trials Fraction - Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
- Safety
Threshold float64 - Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
- desired
Min DoubleSafe Trials Fraction - Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
- safety
Threshold Double - Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
- desired
Min numberSafe Trials Fraction - Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
- safety
Threshold number - Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
- desired_
min_ floatsafe_ trials_ fraction - Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
- safety_
threshold float - Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
- desired
Min NumberSafe Trials Fraction - Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
- safety
Threshold Number - Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
GoogleCloudAiplatformV1StudySpecObservationNoise, GoogleCloudAiplatformV1StudySpecObservationNoiseArgs
- Observation
Noise Unspecified - OBSERVATION_NOISE_UNSPECIFIEDThe default noise level chosen by Vertex AI.
- Low
- LOWVertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.
- High
- HIGHVertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.
- Google
Cloud Aiplatform V1Study Spec Observation Noise Observation Noise Unspecified - OBSERVATION_NOISE_UNSPECIFIEDThe default noise level chosen by Vertex AI.
- Google
Cloud Aiplatform V1Study Spec Observation Noise Low - LOWVertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.
- Google
Cloud Aiplatform V1Study Spec Observation Noise High - HIGHVertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.
- Observation
Noise Unspecified - OBSERVATION_NOISE_UNSPECIFIEDThe default noise level chosen by Vertex AI.
- Low
- LOWVertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.
- High
- HIGHVertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.
- Observation
Noise Unspecified - OBSERVATION_NOISE_UNSPECIFIEDThe default noise level chosen by Vertex AI.
- Low
- LOWVertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.
- High
- HIGHVertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.
- OBSERVATION_NOISE_UNSPECIFIED
- OBSERVATION_NOISE_UNSPECIFIEDThe default noise level chosen by Vertex AI.
- LOW
- LOWVertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.
- HIGH
- HIGHVertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.
- "OBSERVATION_NOISE_UNSPECIFIED"
- OBSERVATION_NOISE_UNSPECIFIEDThe default noise level chosen by Vertex AI.
- "LOW"
- LOWVertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.
- "HIGH"
- HIGHVertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.
GoogleCloudAiplatformV1StudySpecParameterSpec, GoogleCloudAiplatformV1StudySpecParameterSpecArgs
- Parameter
Id string - The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
- Categorical
Value Pulumi.Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Categorical Value Spec - The value spec for a 'CATEGORICAL' parameter.
- Conditional
Parameter List<Pulumi.Specs Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec> - A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
- Discrete
Value Pulumi.Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Discrete Value Spec - The value spec for a 'DISCRETE' parameter.
- Double
Value Pulumi.Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Double Value Spec - The value spec for a 'DOUBLE' parameter.
- Integer
Value Pulumi.Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Integer Value Spec - The value spec for an 'INTEGER' parameter.
- Scale
Type Pulumi.Google Native. Aiplatform. V1. Google Cloud Aiplatform V1Study Spec Parameter Spec Scale Type - How the parameter should be scaled. Leave unset for
CATEGORICAL
parameters.
- Parameter
Id string - The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
- Categorical
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Categorical Value Spec - The value spec for a 'CATEGORICAL' parameter.
- Conditional
Parameter []GoogleSpecs Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec - A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
- Discrete
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Discrete Value Spec - The value spec for a 'DISCRETE' parameter.
- Double
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Double Value Spec - The value spec for a 'DOUBLE' parameter.
- Integer
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Integer Value Spec - The value spec for an 'INTEGER' parameter.
- Scale
Type GoogleCloud Aiplatform V1Study Spec Parameter Spec Scale Type - How the parameter should be scaled. Leave unset for
CATEGORICAL
parameters.
- parameter
Id String - The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
- categorical
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Categorical Value Spec - The value spec for a 'CATEGORICAL' parameter.
- conditional
Parameter List<GoogleSpecs Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec> - A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
- discrete
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Discrete Value Spec - The value spec for a 'DISCRETE' parameter.
- double
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Double Value Spec - The value spec for a 'DOUBLE' parameter.
- integer
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Integer Value Spec - The value spec for an 'INTEGER' parameter.
- scale
Type GoogleCloud Aiplatform V1Study Spec Parameter Spec Scale Type - How the parameter should be scaled. Leave unset for
CATEGORICAL
parameters.
- parameter
Id string - The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
- categorical
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Categorical Value Spec - The value spec for a 'CATEGORICAL' parameter.
- conditional
Parameter GoogleSpecs Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec[] - A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
- discrete
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Discrete Value Spec - The value spec for a 'DISCRETE' parameter.
- double
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Double Value Spec - The value spec for a 'DOUBLE' parameter.
- integer
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Integer Value Spec - The value spec for an 'INTEGER' parameter.
- scale
Type GoogleCloud Aiplatform V1Study Spec Parameter Spec Scale Type - How the parameter should be scaled. Leave unset for
CATEGORICAL
parameters.
- parameter_
id str - The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
- categorical_
value_ Googlespec Cloud Aiplatform V1Study Spec Parameter Spec Categorical Value Spec - The value spec for a 'CATEGORICAL' parameter.
- conditional_
parameter_ Sequence[Googlespecs Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec] - A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
- discrete_
value_ Googlespec Cloud Aiplatform V1Study Spec Parameter Spec Discrete Value Spec - The value spec for a 'DISCRETE' parameter.
- double_
value_ Googlespec Cloud Aiplatform V1Study Spec Parameter Spec Double Value Spec - The value spec for a 'DOUBLE' parameter.
- integer_
value_ Googlespec Cloud Aiplatform V1Study Spec Parameter Spec Integer Value Spec - The value spec for an 'INTEGER' parameter.
- scale_
type GoogleCloud Aiplatform V1Study Spec Parameter Spec Scale Type - How the parameter should be scaled. Leave unset for
CATEGORICAL
parameters.
- parameter
Id String - The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
- categorical
Value Property MapSpec - The value spec for a 'CATEGORICAL' parameter.
- conditional
Parameter List<Property Map>Specs - A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
- discrete
Value Property MapSpec - The value spec for a 'DISCRETE' parameter.
- double
Value Property MapSpec - The value spec for a 'DOUBLE' parameter.
- integer
Value Property MapSpec - The value spec for an 'INTEGER' parameter.
- scale
Type "SCALE_TYPE_UNSPECIFIED" | "UNIT_LINEAR_SCALE" | "UNIT_LOG_SCALE" | "UNIT_REVERSE_LOG_SCALE" - How the parameter should be scaled. Leave unset for
CATEGORICAL
parameters.
GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpec, GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpecArgs
- Values List<string>
- The list of possible categories.
- Default
Value string - A default value for a
CATEGORICAL
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- Values []string
- The list of possible categories.
- Default
Value string - A default value for a
CATEGORICAL
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- values List<String>
- The list of possible categories.
- default
Value String - A default value for a
CATEGORICAL
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- values string[]
- The list of possible categories.
- default
Value string - A default value for a
CATEGORICAL
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- values Sequence[str]
- The list of possible categories.
- default_
value str - A default value for a
CATEGORICAL
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- values List<String>
- The list of possible categories.
- default
Value String - A default value for a
CATEGORICAL
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpecResponse, GoogleCloudAiplatformV1StudySpecParameterSpecCategoricalValueSpecResponseArgs
- Default
Value string - A default value for a
CATEGORICAL
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - Values List<string>
- The list of possible categories.
- Default
Value string - A default value for a
CATEGORICAL
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - Values []string
- The list of possible categories.
- default
Value String - A default value for a
CATEGORICAL
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - values List<String>
- The list of possible categories.
- default
Value string - A default value for a
CATEGORICAL
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - values string[]
- The list of possible categories.
- default_
value str - A default value for a
CATEGORICAL
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - values Sequence[str]
- The list of possible categories.
- default
Value String - A default value for a
CATEGORICAL
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - values List<String>
- The list of possible categories.
GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpec, GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecArgs
- Parameter
Spec Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec - The spec for a conditional parameter.
- Parent
Categorical Pulumi.Values Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Categorical Value Condition - The spec for matching values from a parent parameter of
CATEGORICAL
type. - Parent
Discrete Pulumi.Values Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Discrete Value Condition - The spec for matching values from a parent parameter of
DISCRETE
type. - Parent
Int Pulumi.Values Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Int Value Condition - The spec for matching values from a parent parameter of
INTEGER
type.
- Parameter
Spec GoogleCloud Aiplatform V1Study Spec Parameter Spec - The spec for a conditional parameter.
- Parent
Categorical GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Categorical Value Condition - The spec for matching values from a parent parameter of
CATEGORICAL
type. - Parent
Discrete GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Discrete Value Condition - The spec for matching values from a parent parameter of
DISCRETE
type. - Parent
Int GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Int Value Condition - The spec for matching values from a parent parameter of
INTEGER
type.
- parameter
Spec GoogleCloud Aiplatform V1Study Spec Parameter Spec - The spec for a conditional parameter.
- parent
Categorical GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Categorical Value Condition - The spec for matching values from a parent parameter of
CATEGORICAL
type. - parent
Discrete GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Discrete Value Condition - The spec for matching values from a parent parameter of
DISCRETE
type. - parent
Int GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Int Value Condition - The spec for matching values from a parent parameter of
INTEGER
type.
- parameter
Spec GoogleCloud Aiplatform V1Study Spec Parameter Spec - The spec for a conditional parameter.
- parent
Categorical GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Categorical Value Condition - The spec for matching values from a parent parameter of
CATEGORICAL
type. - parent
Discrete GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Discrete Value Condition - The spec for matching values from a parent parameter of
DISCRETE
type. - parent
Int GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Int Value Condition - The spec for matching values from a parent parameter of
INTEGER
type.
- parameter_
spec GoogleCloud Aiplatform V1Study Spec Parameter Spec - The spec for a conditional parameter.
- parent_
categorical_ Googlevalues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Categorical Value Condition - The spec for matching values from a parent parameter of
CATEGORICAL
type. - parent_
discrete_ Googlevalues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Discrete Value Condition - The spec for matching values from a parent parameter of
DISCRETE
type. - parent_
int_ Googlevalues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Int Value Condition - The spec for matching values from a parent parameter of
INTEGER
type.
- parameter
Spec Property Map - The spec for a conditional parameter.
- parent
Categorical Property MapValues - The spec for matching values from a parent parameter of
CATEGORICAL
type. - parent
Discrete Property MapValues - The spec for matching values from a parent parameter of
DISCRETE
type. - parent
Int Property MapValues - The spec for matching values from a parent parameter of
INTEGER
type.
GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueCondition, GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionArgs
- Values List<string>
- Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in
categorical_value_spec
of parent parameter.
- Values []string
- Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in
categorical_value_spec
of parent parameter.
- values List<String>
- Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in
categorical_value_spec
of parent parameter.
- values string[]
- Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in
categorical_value_spec
of parent parameter.
- values Sequence[str]
- Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in
categorical_value_spec
of parent parameter.
- values List<String>
- Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in
categorical_value_spec
of parent parameter.
GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponse, GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecCategoricalValueConditionResponseArgs
- Values List<string>
- Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in
categorical_value_spec
of parent parameter.
- Values []string
- Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in
categorical_value_spec
of parent parameter.
- values List<String>
- Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in
categorical_value_spec
of parent parameter.
- values string[]
- Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in
categorical_value_spec
of parent parameter.
- values Sequence[str]
- Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in
categorical_value_spec
of parent parameter.
- values List<String>
- Matches values of the parent parameter of 'CATEGORICAL' type. All values must exist in
categorical_value_spec
of parent parameter.
GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueCondition, GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionArgs
- Values List<double>
- Matches values of the parent parameter of 'DISCRETE' type. All values must exist in
discrete_value_spec
of parent parameter. The Epsilon of the value matching is 1e-10.
- Values []float64
- Matches values of the parent parameter of 'DISCRETE' type. All values must exist in
discrete_value_spec
of parent parameter. The Epsilon of the value matching is 1e-10.
- values List<Double>
- Matches values of the parent parameter of 'DISCRETE' type. All values must exist in
discrete_value_spec
of parent parameter. The Epsilon of the value matching is 1e-10.
- values number[]
- Matches values of the parent parameter of 'DISCRETE' type. All values must exist in
discrete_value_spec
of parent parameter. The Epsilon of the value matching is 1e-10.
- values Sequence[float]
- Matches values of the parent parameter of 'DISCRETE' type. All values must exist in
discrete_value_spec
of parent parameter. The Epsilon of the value matching is 1e-10.
- values List<Number>
- Matches values of the parent parameter of 'DISCRETE' type. All values must exist in
discrete_value_spec
of parent parameter. The Epsilon of the value matching is 1e-10.
GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponse, GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecDiscreteValueConditionResponseArgs
- Values List<double>
- Matches values of the parent parameter of 'DISCRETE' type. All values must exist in
discrete_value_spec
of parent parameter. The Epsilon of the value matching is 1e-10.
- Values []float64
- Matches values of the parent parameter of 'DISCRETE' type. All values must exist in
discrete_value_spec
of parent parameter. The Epsilon of the value matching is 1e-10.
- values List<Double>
- Matches values of the parent parameter of 'DISCRETE' type. All values must exist in
discrete_value_spec
of parent parameter. The Epsilon of the value matching is 1e-10.
- values number[]
- Matches values of the parent parameter of 'DISCRETE' type. All values must exist in
discrete_value_spec
of parent parameter. The Epsilon of the value matching is 1e-10.
- values Sequence[float]
- Matches values of the parent parameter of 'DISCRETE' type. All values must exist in
discrete_value_spec
of parent parameter. The Epsilon of the value matching is 1e-10.
- values List<Number>
- Matches values of the parent parameter of 'DISCRETE' type. All values must exist in
discrete_value_spec
of parent parameter. The Epsilon of the value matching is 1e-10.
GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueCondition, GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueConditionArgs
- Values List<string>
- Matches values of the parent parameter of 'INTEGER' type. All values must lie in
integer_value_spec
of parent parameter.
- Values []string
- Matches values of the parent parameter of 'INTEGER' type. All values must lie in
integer_value_spec
of parent parameter.
- values List<String>
- Matches values of the parent parameter of 'INTEGER' type. All values must lie in
integer_value_spec
of parent parameter.
- values string[]
- Matches values of the parent parameter of 'INTEGER' type. All values must lie in
integer_value_spec
of parent parameter.
- values Sequence[str]
- Matches values of the parent parameter of 'INTEGER' type. All values must lie in
integer_value_spec
of parent parameter.
- values List<String>
- Matches values of the parent parameter of 'INTEGER' type. All values must lie in
integer_value_spec
of parent parameter.
GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponse, GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecIntValueConditionResponseArgs
- Values List<string>
- Matches values of the parent parameter of 'INTEGER' type. All values must lie in
integer_value_spec
of parent parameter.
- Values []string
- Matches values of the parent parameter of 'INTEGER' type. All values must lie in
integer_value_spec
of parent parameter.
- values List<String>
- Matches values of the parent parameter of 'INTEGER' type. All values must lie in
integer_value_spec
of parent parameter.
- values string[]
- Matches values of the parent parameter of 'INTEGER' type. All values must lie in
integer_value_spec
of parent parameter.
- values Sequence[str]
- Matches values of the parent parameter of 'INTEGER' type. All values must lie in
integer_value_spec
of parent parameter.
- values List<String>
- Matches values of the parent parameter of 'INTEGER' type. All values must lie in
integer_value_spec
of parent parameter.
GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecResponse, GoogleCloudAiplatformV1StudySpecParameterSpecConditionalParameterSpecResponseArgs
- Parameter
Spec Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Response - The spec for a conditional parameter.
- Parent
Categorical Pulumi.Values Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Categorical Value Condition Response - The spec for matching values from a parent parameter of
CATEGORICAL
type. - Parent
Discrete Pulumi.Values Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Discrete Value Condition Response - The spec for matching values from a parent parameter of
DISCRETE
type. - Parent
Int Pulumi.Values Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Int Value Condition Response - The spec for matching values from a parent parameter of
INTEGER
type.
- Parameter
Spec GoogleCloud Aiplatform V1Study Spec Parameter Spec Response - The spec for a conditional parameter.
- Parent
Categorical GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Categorical Value Condition Response - The spec for matching values from a parent parameter of
CATEGORICAL
type. - Parent
Discrete GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Discrete Value Condition Response - The spec for matching values from a parent parameter of
DISCRETE
type. - Parent
Int GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Int Value Condition Response - The spec for matching values from a parent parameter of
INTEGER
type.
- parameter
Spec GoogleCloud Aiplatform V1Study Spec Parameter Spec Response - The spec for a conditional parameter.
- parent
Categorical GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Categorical Value Condition Response - The spec for matching values from a parent parameter of
CATEGORICAL
type. - parent
Discrete GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Discrete Value Condition Response - The spec for matching values from a parent parameter of
DISCRETE
type. - parent
Int GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Int Value Condition Response - The spec for matching values from a parent parameter of
INTEGER
type.
- parameter
Spec GoogleCloud Aiplatform V1Study Spec Parameter Spec Response - The spec for a conditional parameter.
- parent
Categorical GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Categorical Value Condition Response - The spec for matching values from a parent parameter of
CATEGORICAL
type. - parent
Discrete GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Discrete Value Condition Response - The spec for matching values from a parent parameter of
DISCRETE
type. - parent
Int GoogleValues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Int Value Condition Response - The spec for matching values from a parent parameter of
INTEGER
type.
- parameter_
spec GoogleCloud Aiplatform V1Study Spec Parameter Spec Response - The spec for a conditional parameter.
- parent_
categorical_ Googlevalues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Categorical Value Condition Response - The spec for matching values from a parent parameter of
CATEGORICAL
type. - parent_
discrete_ Googlevalues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Discrete Value Condition Response - The spec for matching values from a parent parameter of
DISCRETE
type. - parent_
int_ Googlevalues Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Int Value Condition Response - The spec for matching values from a parent parameter of
INTEGER
type.
- parameter
Spec Property Map - The spec for a conditional parameter.
- parent
Categorical Property MapValues - The spec for matching values from a parent parameter of
CATEGORICAL
type. - parent
Discrete Property MapValues - The spec for matching values from a parent parameter of
DISCRETE
type. - parent
Int Property MapValues - The spec for matching values from a parent parameter of
INTEGER
type.
GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpec, GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpecArgs
- Values List<double>
- A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
- Default
Value double - A default value for a
DISCRETE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- Values []float64
- A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
- Default
Value float64 - A default value for a
DISCRETE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- values List<Double>
- A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
- default
Value Double - A default value for a
DISCRETE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- values number[]
- A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
- default
Value number - A default value for a
DISCRETE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- values Sequence[float]
- A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
- default_
value float - A default value for a
DISCRETE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- values List<Number>
- A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
- default
Value Number - A default value for a
DISCRETE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpecResponse, GoogleCloudAiplatformV1StudySpecParameterSpecDiscreteValueSpecResponseArgs
- Default
Value double - A default value for a
DISCRETE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - Values List<double>
- A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
- Default
Value float64 - A default value for a
DISCRETE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - Values []float64
- A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
- default
Value Double - A default value for a
DISCRETE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - values List<Double>
- A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
- default
Value number - A default value for a
DISCRETE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - values number[]
- A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
- default_
value float - A default value for a
DISCRETE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - values Sequence[float]
- A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
- default
Value Number - A default value for a
DISCRETE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - values List<Number>
- A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpec, GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpecArgs
- Max
Value double - Inclusive maximum value of the parameter.
- Min
Value double - Inclusive minimum value of the parameter.
- Default
Value double - A default value for a
DOUBLE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- Max
Value float64 - Inclusive maximum value of the parameter.
- Min
Value float64 - Inclusive minimum value of the parameter.
- Default
Value float64 - A default value for a
DOUBLE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- max
Value Double - Inclusive maximum value of the parameter.
- min
Value Double - Inclusive minimum value of the parameter.
- default
Value Double - A default value for a
DOUBLE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- max
Value number - Inclusive maximum value of the parameter.
- min
Value number - Inclusive minimum value of the parameter.
- default
Value number - A default value for a
DOUBLE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- max_
value float - Inclusive maximum value of the parameter.
- min_
value float - Inclusive minimum value of the parameter.
- default_
value float - A default value for a
DOUBLE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- max
Value Number - Inclusive maximum value of the parameter.
- min
Value Number - Inclusive minimum value of the parameter.
- default
Value Number - A default value for a
DOUBLE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpecResponse, GoogleCloudAiplatformV1StudySpecParameterSpecDoubleValueSpecResponseArgs
- Default
Value double - A default value for a
DOUBLE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - Max
Value double - Inclusive maximum value of the parameter.
- Min
Value double - Inclusive minimum value of the parameter.
- Default
Value float64 - A default value for a
DOUBLE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - Max
Value float64 - Inclusive maximum value of the parameter.
- Min
Value float64 - Inclusive minimum value of the parameter.
- default
Value Double - A default value for a
DOUBLE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - max
Value Double - Inclusive maximum value of the parameter.
- min
Value Double - Inclusive minimum value of the parameter.
- default
Value number - A default value for a
DOUBLE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - max
Value number - Inclusive maximum value of the parameter.
- min
Value number - Inclusive minimum value of the parameter.
- default_
value float - A default value for a
DOUBLE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - max_
value float - Inclusive maximum value of the parameter.
- min_
value float - Inclusive minimum value of the parameter.
- default
Value Number - A default value for a
DOUBLE
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - max
Value Number - Inclusive maximum value of the parameter.
- min
Value Number - Inclusive minimum value of the parameter.
GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpec, GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpecArgs
- Max
Value string - Inclusive maximum value of the parameter.
- Min
Value string - Inclusive minimum value of the parameter.
- Default
Value string - A default value for an
INTEGER
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- Max
Value string - Inclusive maximum value of the parameter.
- Min
Value string - Inclusive minimum value of the parameter.
- Default
Value string - A default value for an
INTEGER
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- max
Value String - Inclusive maximum value of the parameter.
- min
Value String - Inclusive minimum value of the parameter.
- default
Value String - A default value for an
INTEGER
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- max
Value string - Inclusive maximum value of the parameter.
- min
Value string - Inclusive minimum value of the parameter.
- default
Value string - A default value for an
INTEGER
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- max_
value str - Inclusive maximum value of the parameter.
- min_
value str - Inclusive minimum value of the parameter.
- default_
value str - A default value for an
INTEGER
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- max
Value String - Inclusive maximum value of the parameter.
- min
Value String - Inclusive minimum value of the parameter.
- default
Value String - A default value for an
INTEGER
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpecResponse, GoogleCloudAiplatformV1StudySpecParameterSpecIntegerValueSpecResponseArgs
- Default
Value string - A default value for an
INTEGER
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - Max
Value string - Inclusive maximum value of the parameter.
- Min
Value string - Inclusive minimum value of the parameter.
- Default
Value string - A default value for an
INTEGER
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - Max
Value string - Inclusive maximum value of the parameter.
- Min
Value string - Inclusive minimum value of the parameter.
- default
Value String - A default value for an
INTEGER
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - max
Value String - Inclusive maximum value of the parameter.
- min
Value String - Inclusive minimum value of the parameter.
- default
Value string - A default value for an
INTEGER
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - max
Value string - Inclusive maximum value of the parameter.
- min
Value string - Inclusive minimum value of the parameter.
- default_
value str - A default value for an
INTEGER
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - max_
value str - Inclusive maximum value of the parameter.
- min_
value str - Inclusive minimum value of the parameter.
- default
Value String - A default value for an
INTEGER
parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline. - max
Value String - Inclusive maximum value of the parameter.
- min
Value String - Inclusive minimum value of the parameter.
GoogleCloudAiplatformV1StudySpecParameterSpecResponse, GoogleCloudAiplatformV1StudySpecParameterSpecResponseArgs
- Categorical
Value Pulumi.Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Categorical Value Spec Response - The value spec for a 'CATEGORICAL' parameter.
- Conditional
Parameter List<Pulumi.Specs Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Response> - A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
- Discrete
Value Pulumi.Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Discrete Value Spec Response - The value spec for a 'DISCRETE' parameter.
- Double
Value Pulumi.Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Double Value Spec Response - The value spec for a 'DOUBLE' parameter.
- Integer
Value Pulumi.Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Integer Value Spec Response - The value spec for an 'INTEGER' parameter.
- Parameter
Id string - The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
- Scale
Type string - How the parameter should be scaled. Leave unset for
CATEGORICAL
parameters.
- Categorical
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Categorical Value Spec Response - The value spec for a 'CATEGORICAL' parameter.
- Conditional
Parameter []GoogleSpecs Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Response - A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
- Discrete
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Discrete Value Spec Response - The value spec for a 'DISCRETE' parameter.
- Double
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Double Value Spec Response - The value spec for a 'DOUBLE' parameter.
- Integer
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Integer Value Spec Response - The value spec for an 'INTEGER' parameter.
- Parameter
Id string - The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
- Scale
Type string - How the parameter should be scaled. Leave unset for
CATEGORICAL
parameters.
- categorical
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Categorical Value Spec Response - The value spec for a 'CATEGORICAL' parameter.
- conditional
Parameter List<GoogleSpecs Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Response> - A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
- discrete
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Discrete Value Spec Response - The value spec for a 'DISCRETE' parameter.
- double
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Double Value Spec Response - The value spec for a 'DOUBLE' parameter.
- integer
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Integer Value Spec Response - The value spec for an 'INTEGER' parameter.
- parameter
Id String - The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
- scale
Type String - How the parameter should be scaled. Leave unset for
CATEGORICAL
parameters.
- categorical
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Categorical Value Spec Response - The value spec for a 'CATEGORICAL' parameter.
- conditional
Parameter GoogleSpecs Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Response[] - A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
- discrete
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Discrete Value Spec Response - The value spec for a 'DISCRETE' parameter.
- double
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Double Value Spec Response - The value spec for a 'DOUBLE' parameter.
- integer
Value GoogleSpec Cloud Aiplatform V1Study Spec Parameter Spec Integer Value Spec Response - The value spec for an 'INTEGER' parameter.
- parameter
Id string - The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
- scale
Type string - How the parameter should be scaled. Leave unset for
CATEGORICAL
parameters.
- categorical_
value_ Googlespec Cloud Aiplatform V1Study Spec Parameter Spec Categorical Value Spec Response - The value spec for a 'CATEGORICAL' parameter.
- conditional_
parameter_ Sequence[Googlespecs Cloud Aiplatform V1Study Spec Parameter Spec Conditional Parameter Spec Response] - A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
- discrete_
value_ Googlespec Cloud Aiplatform V1Study Spec Parameter Spec Discrete Value Spec Response - The value spec for a 'DISCRETE' parameter.
- double_
value_ Googlespec Cloud Aiplatform V1Study Spec Parameter Spec Double Value Spec Response - The value spec for a 'DOUBLE' parameter.
- integer_
value_ Googlespec Cloud Aiplatform V1Study Spec Parameter Spec Integer Value Spec Response - The value spec for an 'INTEGER' parameter.
- parameter_
id str - The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
- scale_
type str - How the parameter should be scaled. Leave unset for
CATEGORICAL
parameters.
- categorical
Value Property MapSpec - The value spec for a 'CATEGORICAL' parameter.
- conditional
Parameter List<Property Map>Specs - A conditional parameter node is active if the parameter's value matches the conditional node's parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
- discrete
Value Property MapSpec - The value spec for a 'DISCRETE' parameter.
- double
Value Property MapSpec - The value spec for a 'DOUBLE' parameter.
- integer
Value Property MapSpec - The value spec for an 'INTEGER' parameter.
- parameter
Id String - The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
- scale
Type String - How the parameter should be scaled. Leave unset for
CATEGORICAL
parameters.
GoogleCloudAiplatformV1StudySpecParameterSpecScaleType, GoogleCloudAiplatformV1StudySpecParameterSpecScaleTypeArgs
- Scale
Type Unspecified - SCALE_TYPE_UNSPECIFIEDBy default, no scaling is applied.
- Unit
Linear Scale - UNIT_LINEAR_SCALEScales the feasible space to (0, 1) linearly.
- Unit
Log Scale - UNIT_LOG_SCALEScales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
- Unit
Reverse Log Scale - UNIT_REVERSE_LOG_SCALEScales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.
- Google
Cloud Aiplatform V1Study Spec Parameter Spec Scale Type Scale Type Unspecified - SCALE_TYPE_UNSPECIFIEDBy default, no scaling is applied.
- Google
Cloud Aiplatform V1Study Spec Parameter Spec Scale Type Unit Linear Scale - UNIT_LINEAR_SCALEScales the feasible space to (0, 1) linearly.
- Google
Cloud Aiplatform V1Study Spec Parameter Spec Scale Type Unit Log Scale - UNIT_LOG_SCALEScales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
- Google
Cloud Aiplatform V1Study Spec Parameter Spec Scale Type Unit Reverse Log Scale - UNIT_REVERSE_LOG_SCALEScales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.
- Scale
Type Unspecified - SCALE_TYPE_UNSPECIFIEDBy default, no scaling is applied.
- Unit
Linear Scale - UNIT_LINEAR_SCALEScales the feasible space to (0, 1) linearly.
- Unit
Log Scale - UNIT_LOG_SCALEScales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
- Unit
Reverse Log Scale - UNIT_REVERSE_LOG_SCALEScales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.
- Scale
Type Unspecified - SCALE_TYPE_UNSPECIFIEDBy default, no scaling is applied.
- Unit
Linear Scale - UNIT_LINEAR_SCALEScales the feasible space to (0, 1) linearly.
- Unit
Log Scale - UNIT_LOG_SCALEScales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
- Unit
Reverse Log Scale - UNIT_REVERSE_LOG_SCALEScales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.
- SCALE_TYPE_UNSPECIFIED
- SCALE_TYPE_UNSPECIFIEDBy default, no scaling is applied.
- UNIT_LINEAR_SCALE
- UNIT_LINEAR_SCALEScales the feasible space to (0, 1) linearly.
- UNIT_LOG_SCALE
- UNIT_LOG_SCALEScales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
- UNIT_REVERSE_LOG_SCALE
- UNIT_REVERSE_LOG_SCALEScales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.
- "SCALE_TYPE_UNSPECIFIED"
- SCALE_TYPE_UNSPECIFIEDBy default, no scaling is applied.
- "UNIT_LINEAR_SCALE"
- UNIT_LINEAR_SCALEScales the feasible space to (0, 1) linearly.
- "UNIT_LOG_SCALE"
- UNIT_LOG_SCALEScales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
- "UNIT_REVERSE_LOG_SCALE"
- UNIT_REVERSE_LOG_SCALEScales the feasible space "reverse" logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.
GoogleCloudAiplatformV1StudySpecResponse, GoogleCloudAiplatformV1StudySpecResponseArgs
- Algorithm string
- The search algorithm specified for the Study.
- Convex
Automated Pulumi.Stopping Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Convex Automated Stopping Spec Response - The automated early stopping spec using convex stopping rule.
- Decay
Curve Pulumi.Stopping Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Decay Curve Automated Stopping Spec Response - The automated early stopping spec using decay curve rule.
- Measurement
Selection stringType - Describe which measurement selection type will be used
- Median
Automated Pulumi.Stopping Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Median Automated Stopping Spec Response - The automated early stopping spec using median rule.
- Metrics
List<Pulumi.
Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Metric Spec Response> - Metric specs for the Study.
- Observation
Noise string - The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- Parameters
List<Pulumi.
Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Parameter Spec Response> - The set of parameters to tune.
- Study
Stopping Pulumi.Config Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Spec Study Stopping Config Response - Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
- Algorithm string
- The search algorithm specified for the Study.
- Convex
Automated GoogleStopping Spec Cloud Aiplatform V1Study Spec Convex Automated Stopping Spec Response - The automated early stopping spec using convex stopping rule.
- Decay
Curve GoogleStopping Spec Cloud Aiplatform V1Study Spec Decay Curve Automated Stopping Spec Response - The automated early stopping spec using decay curve rule.
- Measurement
Selection stringType - Describe which measurement selection type will be used
- Median
Automated GoogleStopping Spec Cloud Aiplatform V1Study Spec Median Automated Stopping Spec Response - The automated early stopping spec using median rule.
- Metrics
[]Google
Cloud Aiplatform V1Study Spec Metric Spec Response - Metric specs for the Study.
- Observation
Noise string - The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- Parameters
[]Google
Cloud Aiplatform V1Study Spec Parameter Spec Response - The set of parameters to tune.
- Study
Stopping GoogleConfig Cloud Aiplatform V1Study Spec Study Stopping Config Response - Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
- algorithm String
- The search algorithm specified for the Study.
- convex
Automated GoogleStopping Spec Cloud Aiplatform V1Study Spec Convex Automated Stopping Spec Response - The automated early stopping spec using convex stopping rule.
- decay
Curve GoogleStopping Spec Cloud Aiplatform V1Study Spec Decay Curve Automated Stopping Spec Response - The automated early stopping spec using decay curve rule.
- measurement
Selection StringType - Describe which measurement selection type will be used
- median
Automated GoogleStopping Spec Cloud Aiplatform V1Study Spec Median Automated Stopping Spec Response - The automated early stopping spec using median rule.
- metrics
List<Google
Cloud Aiplatform V1Study Spec Metric Spec Response> - Metric specs for the Study.
- observation
Noise String - The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- parameters
List<Google
Cloud Aiplatform V1Study Spec Parameter Spec Response> - The set of parameters to tune.
- study
Stopping GoogleConfig Cloud Aiplatform V1Study Spec Study Stopping Config Response - Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
- algorithm string
- The search algorithm specified for the Study.
- convex
Automated GoogleStopping Spec Cloud Aiplatform V1Study Spec Convex Automated Stopping Spec Response - The automated early stopping spec using convex stopping rule.
- decay
Curve GoogleStopping Spec Cloud Aiplatform V1Study Spec Decay Curve Automated Stopping Spec Response - The automated early stopping spec using decay curve rule.
- measurement
Selection stringType - Describe which measurement selection type will be used
- median
Automated GoogleStopping Spec Cloud Aiplatform V1Study Spec Median Automated Stopping Spec Response - The automated early stopping spec using median rule.
- metrics
Google
Cloud Aiplatform V1Study Spec Metric Spec Response[] - Metric specs for the Study.
- observation
Noise string - The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- parameters
Google
Cloud Aiplatform V1Study Spec Parameter Spec Response[] - The set of parameters to tune.
- study
Stopping GoogleConfig Cloud Aiplatform V1Study Spec Study Stopping Config Response - Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
- algorithm str
- The search algorithm specified for the Study.
- convex_
automated_ Googlestopping_ spec Cloud Aiplatform V1Study Spec Convex Automated Stopping Spec Response - The automated early stopping spec using convex stopping rule.
- decay_
curve_ Googlestopping_ spec Cloud Aiplatform V1Study Spec Decay Curve Automated Stopping Spec Response - The automated early stopping spec using decay curve rule.
- measurement_
selection_ strtype - Describe which measurement selection type will be used
- median_
automated_ Googlestopping_ spec Cloud Aiplatform V1Study Spec Median Automated Stopping Spec Response - The automated early stopping spec using median rule.
- metrics
Sequence[Google
Cloud Aiplatform V1Study Spec Metric Spec Response] - Metric specs for the Study.
- observation_
noise str - The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- parameters
Sequence[Google
Cloud Aiplatform V1Study Spec Parameter Spec Response] - The set of parameters to tune.
- study_
stopping_ Googleconfig Cloud Aiplatform V1Study Spec Study Stopping Config Response - Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
- algorithm String
- The search algorithm specified for the Study.
- convex
Automated Property MapStopping Spec - The automated early stopping spec using convex stopping rule.
- decay
Curve Property MapStopping Spec - The automated early stopping spec using decay curve rule.
- measurement
Selection StringType - Describe which measurement selection type will be used
- median
Automated Property MapStopping Spec - The automated early stopping spec using median rule.
- metrics List<Property Map>
- Metric specs for the Study.
- observation
Noise String - The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
- parameters List<Property Map>
- The set of parameters to tune.
- study
Stopping Property MapConfig - Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
GoogleCloudAiplatformV1StudySpecStudyStoppingConfig, GoogleCloudAiplatformV1StudySpecStudyStoppingConfigArgs
- Max
Duration stringNo Progress - If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
- Max
Num intTrials - If there are more than this many trials, stop the study.
- Max
Num intTrials No Progress - If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
- Maximum
Runtime Pulumi.Constraint Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Time Constraint - If the specified time or duration has passed, stop the study.
- Min
Num intTrials - If there are fewer than this many COMPLETED trials, do not stop the study.
- Minimum
Runtime Pulumi.Constraint Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Time Constraint - Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting
min_num_trials=5
andalways_stop_after= 1 hour
means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study. - Should
Stop boolAsap - If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
- Max
Duration stringNo Progress - If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
- Max
Num intTrials - If there are more than this many trials, stop the study.
- Max
Num intTrials No Progress - If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
- Maximum
Runtime GoogleConstraint Cloud Aiplatform V1Study Time Constraint - If the specified time or duration has passed, stop the study.
- Min
Num intTrials - If there are fewer than this many COMPLETED trials, do not stop the study.
- Minimum
Runtime GoogleConstraint Cloud Aiplatform V1Study Time Constraint - Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting
min_num_trials=5
andalways_stop_after= 1 hour
means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study. - Should
Stop boolAsap - If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
- max
Duration StringNo Progress - If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
- max
Num IntegerTrials - If there are more than this many trials, stop the study.
- max
Num IntegerTrials No Progress - If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
- maximum
Runtime GoogleConstraint Cloud Aiplatform V1Study Time Constraint - If the specified time or duration has passed, stop the study.
- min
Num IntegerTrials - If there are fewer than this many COMPLETED trials, do not stop the study.
- minimum
Runtime GoogleConstraint Cloud Aiplatform V1Study Time Constraint - Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting
min_num_trials=5
andalways_stop_after= 1 hour
means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study. - should
Stop BooleanAsap - If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
- max
Duration stringNo Progress - If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
- max
Num numberTrials - If there are more than this many trials, stop the study.
- max
Num numberTrials No Progress - If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
- maximum
Runtime GoogleConstraint Cloud Aiplatform V1Study Time Constraint - If the specified time or duration has passed, stop the study.
- min
Num numberTrials - If there are fewer than this many COMPLETED trials, do not stop the study.
- minimum
Runtime GoogleConstraint Cloud Aiplatform V1Study Time Constraint - Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting
min_num_trials=5
andalways_stop_after= 1 hour
means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study. - should
Stop booleanAsap - If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
- max_
duration_ strno_ progress - If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
- max_
num_ inttrials - If there are more than this many trials, stop the study.
- max_
num_ inttrials_ no_ progress - If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
- maximum_
runtime_ Googleconstraint Cloud Aiplatform V1Study Time Constraint - If the specified time or duration has passed, stop the study.
- min_
num_ inttrials - If there are fewer than this many COMPLETED trials, do not stop the study.
- minimum_
runtime_ Googleconstraint Cloud Aiplatform V1Study Time Constraint - Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting
min_num_trials=5
andalways_stop_after= 1 hour
means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study. - should_
stop_ boolasap - If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
- max
Duration StringNo Progress - If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
- max
Num NumberTrials - If there are more than this many trials, stop the study.
- max
Num NumberTrials No Progress - If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
- maximum
Runtime Property MapConstraint - If the specified time or duration has passed, stop the study.
- min
Num NumberTrials - If there are fewer than this many COMPLETED trials, do not stop the study.
- minimum
Runtime Property MapConstraint - Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting
min_num_trials=5
andalways_stop_after= 1 hour
means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study. - should
Stop BooleanAsap - If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
GoogleCloudAiplatformV1StudySpecStudyStoppingConfigResponse, GoogleCloudAiplatformV1StudySpecStudyStoppingConfigResponseArgs
- Max
Duration stringNo Progress - If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
- Max
Num intTrials - If there are more than this many trials, stop the study.
- Max
Num intTrials No Progress - If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
- Maximum
Runtime Pulumi.Constraint Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Time Constraint Response - If the specified time or duration has passed, stop the study.
- Min
Num intTrials - If there are fewer than this many COMPLETED trials, do not stop the study.
- Minimum
Runtime Pulumi.Constraint Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Study Time Constraint Response - Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting
min_num_trials=5
andalways_stop_after= 1 hour
means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study. - Should
Stop boolAsap - If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
- Max
Duration stringNo Progress - If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
- Max
Num intTrials - If there are more than this many trials, stop the study.
- Max
Num intTrials No Progress - If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
- Maximum
Runtime GoogleConstraint Cloud Aiplatform V1Study Time Constraint Response - If the specified time or duration has passed, stop the study.
- Min
Num intTrials - If there are fewer than this many COMPLETED trials, do not stop the study.
- Minimum
Runtime GoogleConstraint Cloud Aiplatform V1Study Time Constraint Response - Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting
min_num_trials=5
andalways_stop_after= 1 hour
means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study. - Should
Stop boolAsap - If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
- max
Duration StringNo Progress - If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
- max
Num IntegerTrials - If there are more than this many trials, stop the study.
- max
Num IntegerTrials No Progress - If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
- maximum
Runtime GoogleConstraint Cloud Aiplatform V1Study Time Constraint Response - If the specified time or duration has passed, stop the study.
- min
Num IntegerTrials - If there are fewer than this many COMPLETED trials, do not stop the study.
- minimum
Runtime GoogleConstraint Cloud Aiplatform V1Study Time Constraint Response - Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting
min_num_trials=5
andalways_stop_after= 1 hour
means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study. - should
Stop BooleanAsap - If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
- max
Duration stringNo Progress - If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
- max
Num numberTrials - If there are more than this many trials, stop the study.
- max
Num numberTrials No Progress - If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
- maximum
Runtime GoogleConstraint Cloud Aiplatform V1Study Time Constraint Response - If the specified time or duration has passed, stop the study.
- min
Num numberTrials - If there are fewer than this many COMPLETED trials, do not stop the study.
- minimum
Runtime GoogleConstraint Cloud Aiplatform V1Study Time Constraint Response - Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting
min_num_trials=5
andalways_stop_after= 1 hour
means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study. - should
Stop booleanAsap - If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
- max_
duration_ strno_ progress - If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
- max_
num_ inttrials - If there are more than this many trials, stop the study.
- max_
num_ inttrials_ no_ progress - If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
- maximum_
runtime_ Googleconstraint Cloud Aiplatform V1Study Time Constraint Response - If the specified time or duration has passed, stop the study.
- min_
num_ inttrials - If there are fewer than this many COMPLETED trials, do not stop the study.
- minimum_
runtime_ Googleconstraint Cloud Aiplatform V1Study Time Constraint Response - Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting
min_num_trials=5
andalways_stop_after= 1 hour
means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study. - should_
stop_ boolasap - If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
- max
Duration StringNo Progress - If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
- max
Num NumberTrials - If there are more than this many trials, stop the study.
- max
Num NumberTrials No Progress - If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
- maximum
Runtime Property MapConstraint - If the specified time or duration has passed, stop the study.
- min
Num NumberTrials - If there are fewer than this many COMPLETED trials, do not stop the study.
- minimum
Runtime Property MapConstraint - Each "stopping rule" in this proto specifies an "if" condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting
min_num_trials=5
andalways_stop_after= 1 hour
means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose "if" condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study. - should
Stop BooleanAsap - If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
GoogleCloudAiplatformV1StudyTimeConstraint, GoogleCloudAiplatformV1StudyTimeConstraintArgs
- End
Time string - Compares the wallclock time to this time. Must use UTC timezone.
- Max
Duration string - Counts the wallclock time passed since the creation of this Study.
- End
Time string - Compares the wallclock time to this time. Must use UTC timezone.
- Max
Duration string - Counts the wallclock time passed since the creation of this Study.
- end
Time String - Compares the wallclock time to this time. Must use UTC timezone.
- max
Duration String - Counts the wallclock time passed since the creation of this Study.
- end
Time string - Compares the wallclock time to this time. Must use UTC timezone.
- max
Duration string - Counts the wallclock time passed since the creation of this Study.
- end_
time str - Compares the wallclock time to this time. Must use UTC timezone.
- max_
duration str - Counts the wallclock time passed since the creation of this Study.
- end
Time String - Compares the wallclock time to this time. Must use UTC timezone.
- max
Duration String - Counts the wallclock time passed since the creation of this Study.
GoogleCloudAiplatformV1StudyTimeConstraintResponse, GoogleCloudAiplatformV1StudyTimeConstraintResponseArgs
- End
Time string - Compares the wallclock time to this time. Must use UTC timezone.
- Max
Duration string - Counts the wallclock time passed since the creation of this Study.
- End
Time string - Compares the wallclock time to this time. Must use UTC timezone.
- Max
Duration string - Counts the wallclock time passed since the creation of this Study.
- end
Time String - Compares the wallclock time to this time. Must use UTC timezone.
- max
Duration String - Counts the wallclock time passed since the creation of this Study.
- end
Time string - Compares the wallclock time to this time. Must use UTC timezone.
- max
Duration string - Counts the wallclock time passed since the creation of this Study.
- end_
time str - Compares the wallclock time to this time. Must use UTC timezone.
- max_
duration str - Counts the wallclock time passed since the creation of this Study.
- end
Time String - Compares the wallclock time to this time. Must use UTC timezone.
- max
Duration String - Counts the wallclock time passed since the creation of this Study.
GoogleCloudAiplatformV1TrialParameterResponse, GoogleCloudAiplatformV1TrialParameterResponseArgs
- Parameter
Id string - The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
- Value object
- The value of the parameter.
number_value
will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'.string_value
will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
- Parameter
Id string - The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
- Value interface{}
- The value of the parameter.
number_value
will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'.string_value
will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
- parameter
Id String - The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
- value Object
- The value of the parameter.
number_value
will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'.string_value
will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
- parameter
Id string - The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
- value any
- The value of the parameter.
number_value
will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'.string_value
will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
- parameter_
id str - The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
- value Any
- The value of the parameter.
number_value
will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'.string_value
will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
- parameter
Id String - The ID of the parameter. The parameter should be defined in StudySpec's Parameters.
- value Any
- The value of the parameter.
number_value
will be set if a parameter defined in StudySpec is in type 'INTEGER', 'DOUBLE' or 'DISCRETE'.string_value
will be set if a parameter defined in StudySpec is in type 'CATEGORICAL'.
GoogleCloudAiplatformV1TrialResponse, GoogleCloudAiplatformV1TrialResponseArgs
- Client
Id string - The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
- Custom
Job string - The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
- End
Time string - Time when the Trial's status changed to
SUCCEEDED
orINFEASIBLE
. - Final
Measurement Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Measurement Response - The final measurement containing the objective value.
- Infeasible
Reason string - A human readable string describing why the Trial is infeasible. This is set only if Trial state is
INFEASIBLE
. - Measurements
List<Pulumi.
Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Measurement Response> - A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
- Name string
- Resource name of the Trial assigned by the service.
- Parameters
List<Pulumi.
Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Trial Parameter Response> - The parameters of the Trial.
- Start
Time string - Time when the Trial was started.
- State string
- The detailed state of the Trial.
- Web
Access Dictionary<string, string>Uris - URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is
true
. The keys are names of each node used for the trial; for example,workerpool0-0
for the primary node,workerpool1-0
for the first node in the second worker pool, andworkerpool1-1
for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
- Client
Id string - The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
- Custom
Job string - The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
- End
Time string - Time when the Trial's status changed to
SUCCEEDED
orINFEASIBLE
. - Final
Measurement GoogleCloud Aiplatform V1Measurement Response - The final measurement containing the objective value.
- Infeasible
Reason string - A human readable string describing why the Trial is infeasible. This is set only if Trial state is
INFEASIBLE
. - Measurements
[]Google
Cloud Aiplatform V1Measurement Response - A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
- Name string
- Resource name of the Trial assigned by the service.
- Parameters
[]Google
Cloud Aiplatform V1Trial Parameter Response - The parameters of the Trial.
- Start
Time string - Time when the Trial was started.
- State string
- The detailed state of the Trial.
- Web
Access map[string]stringUris - URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is
true
. The keys are names of each node used for the trial; for example,workerpool0-0
for the primary node,workerpool1-0
for the first node in the second worker pool, andworkerpool1-1
for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
- client
Id String - The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
- custom
Job String - The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
- end
Time String - Time when the Trial's status changed to
SUCCEEDED
orINFEASIBLE
. - final
Measurement GoogleCloud Aiplatform V1Measurement Response - The final measurement containing the objective value.
- infeasible
Reason String - A human readable string describing why the Trial is infeasible. This is set only if Trial state is
INFEASIBLE
. - measurements
List<Google
Cloud Aiplatform V1Measurement Response> - A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
- name String
- Resource name of the Trial assigned by the service.
- parameters
List<Google
Cloud Aiplatform V1Trial Parameter Response> - The parameters of the Trial.
- start
Time String - Time when the Trial was started.
- state String
- The detailed state of the Trial.
- web
Access Map<String,String>Uris - URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is
true
. The keys are names of each node used for the trial; for example,workerpool0-0
for the primary node,workerpool1-0
for the first node in the second worker pool, andworkerpool1-1
for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
- client
Id string - The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
- custom
Job string - The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
- end
Time string - Time when the Trial's status changed to
SUCCEEDED
orINFEASIBLE
. - final
Measurement GoogleCloud Aiplatform V1Measurement Response - The final measurement containing the objective value.
- infeasible
Reason string - A human readable string describing why the Trial is infeasible. This is set only if Trial state is
INFEASIBLE
. - measurements
Google
Cloud Aiplatform V1Measurement Response[] - A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
- name string
- Resource name of the Trial assigned by the service.
- parameters
Google
Cloud Aiplatform V1Trial Parameter Response[] - The parameters of the Trial.
- start
Time string - Time when the Trial was started.
- state string
- The detailed state of the Trial.
- web
Access {[key: string]: string}Uris - URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is
true
. The keys are names of each node used for the trial; for example,workerpool0-0
for the primary node,workerpool1-0
for the first node in the second worker pool, andworkerpool1-1
for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
- client_
id str - The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
- custom_
job str - The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
- end_
time str - Time when the Trial's status changed to
SUCCEEDED
orINFEASIBLE
. - final_
measurement GoogleCloud Aiplatform V1Measurement Response - The final measurement containing the objective value.
- infeasible_
reason str - A human readable string describing why the Trial is infeasible. This is set only if Trial state is
INFEASIBLE
. - measurements
Sequence[Google
Cloud Aiplatform V1Measurement Response] - A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
- name str
- Resource name of the Trial assigned by the service.
- parameters
Sequence[Google
Cloud Aiplatform V1Trial Parameter Response] - The parameters of the Trial.
- start_
time str - Time when the Trial was started.
- state str
- The detailed state of the Trial.
- web_
access_ Mapping[str, str]uris - URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is
true
. The keys are names of each node used for the trial; for example,workerpool0-0
for the primary node,workerpool1-0
for the first node in the second worker pool, andworkerpool1-1
for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
- client
Id String - The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
- custom
Job String - The CustomJob name linked to the Trial. It's set for a HyperparameterTuningJob's Trial.
- end
Time String - Time when the Trial's status changed to
SUCCEEDED
orINFEASIBLE
. - final
Measurement Property Map - The final measurement containing the objective value.
- infeasible
Reason String - A human readable string describing why the Trial is infeasible. This is set only if Trial state is
INFEASIBLE
. - measurements List<Property Map>
- A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
- name String
- Resource name of the Trial assigned by the service.
- parameters List<Property Map>
- The parameters of the Trial.
- start
Time String - Time when the Trial was started.
- state String
- The detailed state of the Trial.
- web
Access Map<String>Uris - URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a HyperparameterTuningJob and the job's trial_job_spec.enable_web_access field is
true
. The keys are names of each node used for the trial; for example,workerpool0-0
for the primary node,workerpool1-0
for the first node in the second worker pool, andworkerpool1-1
for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
GoogleCloudAiplatformV1WorkerPoolSpec, GoogleCloudAiplatformV1WorkerPoolSpecArgs
- Container
Spec Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Container Spec - The custom container task.
- Disk
Spec Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Disk Spec - Disk spec.
- Machine
Spec Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Machine Spec - Optional. Immutable. The specification of a single machine.
- Nfs
Mounts List<Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Nfs Mount> - Optional. List of NFS mount spec.
- Python
Package Pulumi.Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Python Package Spec - The Python packaged task.
- Replica
Count string - Optional. The number of worker replicas to use for this worker pool.
- Container
Spec GoogleCloud Aiplatform V1Container Spec - The custom container task.
- Disk
Spec GoogleCloud Aiplatform V1Disk Spec - Disk spec.
- Machine
Spec GoogleCloud Aiplatform V1Machine Spec - Optional. Immutable. The specification of a single machine.
- Nfs
Mounts []GoogleCloud Aiplatform V1Nfs Mount - Optional. List of NFS mount spec.
- Python
Package GoogleSpec Cloud Aiplatform V1Python Package Spec - The Python packaged task.
- Replica
Count string - Optional. The number of worker replicas to use for this worker pool.
- container
Spec GoogleCloud Aiplatform V1Container Spec - The custom container task.
- disk
Spec GoogleCloud Aiplatform V1Disk Spec - Disk spec.
- machine
Spec GoogleCloud Aiplatform V1Machine Spec - Optional. Immutable. The specification of a single machine.
- nfs
Mounts List<GoogleCloud Aiplatform V1Nfs Mount> - Optional. List of NFS mount spec.
- python
Package GoogleSpec Cloud Aiplatform V1Python Package Spec - The Python packaged task.
- replica
Count String - Optional. The number of worker replicas to use for this worker pool.
- container
Spec GoogleCloud Aiplatform V1Container Spec - The custom container task.
- disk
Spec GoogleCloud Aiplatform V1Disk Spec - Disk spec.
- machine
Spec GoogleCloud Aiplatform V1Machine Spec - Optional. Immutable. The specification of a single machine.
- nfs
Mounts GoogleCloud Aiplatform V1Nfs Mount[] - Optional. List of NFS mount spec.
- python
Package GoogleSpec Cloud Aiplatform V1Python Package Spec - The Python packaged task.
- replica
Count string - Optional. The number of worker replicas to use for this worker pool.
- container_
spec GoogleCloud Aiplatform V1Container Spec - The custom container task.
- disk_
spec GoogleCloud Aiplatform V1Disk Spec - Disk spec.
- machine_
spec GoogleCloud Aiplatform V1Machine Spec - Optional. Immutable. The specification of a single machine.
- nfs_
mounts Sequence[GoogleCloud Aiplatform V1Nfs Mount] - Optional. List of NFS mount spec.
- python_
package_ Googlespec Cloud Aiplatform V1Python Package Spec - The Python packaged task.
- replica_
count str - Optional. The number of worker replicas to use for this worker pool.
- container
Spec Property Map - The custom container task.
- disk
Spec Property Map - Disk spec.
- machine
Spec Property Map - Optional. Immutable. The specification of a single machine.
- nfs
Mounts List<Property Map> - Optional. List of NFS mount spec.
- python
Package Property MapSpec - The Python packaged task.
- replica
Count String - Optional. The number of worker replicas to use for this worker pool.
GoogleCloudAiplatformV1WorkerPoolSpecResponse, GoogleCloudAiplatformV1WorkerPoolSpecResponseArgs
- Container
Spec Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Container Spec Response - The custom container task.
- Disk
Spec Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Disk Spec Response - Disk spec.
- Machine
Spec Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Machine Spec Response - Optional. Immutable. The specification of a single machine.
- Nfs
Mounts List<Pulumi.Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Nfs Mount Response> - Optional. List of NFS mount spec.
- Python
Package Pulumi.Spec Google Native. Aiplatform. V1. Inputs. Google Cloud Aiplatform V1Python Package Spec Response - The Python packaged task.
- Replica
Count string - Optional. The number of worker replicas to use for this worker pool.
- Container
Spec GoogleCloud Aiplatform V1Container Spec Response - The custom container task.
- Disk
Spec GoogleCloud Aiplatform V1Disk Spec Response - Disk spec.
- Machine
Spec GoogleCloud Aiplatform V1Machine Spec Response - Optional. Immutable. The specification of a single machine.
- Nfs
Mounts []GoogleCloud Aiplatform V1Nfs Mount Response - Optional. List of NFS mount spec.
- Python
Package GoogleSpec Cloud Aiplatform V1Python Package Spec Response - The Python packaged task.
- Replica
Count string - Optional. The number of worker replicas to use for this worker pool.
- container
Spec GoogleCloud Aiplatform V1Container Spec Response - The custom container task.
- disk
Spec GoogleCloud Aiplatform V1Disk Spec Response - Disk spec.
- machine
Spec GoogleCloud Aiplatform V1Machine Spec Response - Optional. Immutable. The specification of a single machine.
- nfs
Mounts List<GoogleCloud Aiplatform V1Nfs Mount Response> - Optional. List of NFS mount spec.
- python
Package GoogleSpec Cloud Aiplatform V1Python Package Spec Response - The Python packaged task.
- replica
Count String - Optional. The number of worker replicas to use for this worker pool.
- container
Spec GoogleCloud Aiplatform V1Container Spec Response - The custom container task.
- disk
Spec GoogleCloud Aiplatform V1Disk Spec Response - Disk spec.
- machine
Spec GoogleCloud Aiplatform V1Machine Spec Response - Optional. Immutable. The specification of a single machine.
- nfs
Mounts GoogleCloud Aiplatform V1Nfs Mount Response[] - Optional. List of NFS mount spec.
- python
Package GoogleSpec Cloud Aiplatform V1Python Package Spec Response - The Python packaged task.
- replica
Count string - Optional. The number of worker replicas to use for this worker pool.
- container_
spec GoogleCloud Aiplatform V1Container Spec Response - The custom container task.
- disk_
spec GoogleCloud Aiplatform V1Disk Spec Response - Disk spec.
- machine_
spec GoogleCloud Aiplatform V1Machine Spec Response - Optional. Immutable. The specification of a single machine.
- nfs_
mounts Sequence[GoogleCloud Aiplatform V1Nfs Mount Response] - Optional. List of NFS mount spec.
- python_
package_ Googlespec Cloud Aiplatform V1Python Package Spec Response - The Python packaged task.
- replica_
count str - Optional. The number of worker replicas to use for this worker pool.
- container
Spec Property Map - The custom container task.
- disk
Spec Property Map - Disk spec.
- machine
Spec Property Map - Optional. Immutable. The specification of a single machine.
- nfs
Mounts List<Property Map> - Optional. List of NFS mount spec.
- python
Package Property MapSpec - The Python packaged task.
- replica
Count String - Optional. The number of worker replicas to use for this worker pool.
GoogleRpcStatusResponse, GoogleRpcStatusResponseArgs
- Code int
- The status code, which should be an enum value of google.rpc.Code.