pulumi-google-native-kotlin/com.pulumi.googlenative.ml.v1.kotlin.inputs/GoogleCloudMlV1__HyperparameterSpecArgs

GoogleCloudMlV1__HyperparameterSpecArgs

data class GoogleCloudMlV1__HyperparameterSpecArgs(val algorithm: Output<GoogleCloudMlV1__HyperparameterSpecAlgorithm>? = null, val enableTrialEarlyStopping: Output<Boolean>? = null, val goal: Output<GoogleCloudMlV1__HyperparameterSpecGoal>, val hyperparameterMetricTag: Output<String>? = null, val maxFailedTrials: Output<Int>? = null, val maxParallelTrials: Output<Int>? = null, val maxTrials: Output<Int>? = null, val params: Output<List<GoogleCloudMlV1__ParameterSpecArgs>>, val resumePreviousJobId: Output<String>? = null) : ConvertibleToJava<GoogleCloudMlV1__HyperparameterSpecArgs>

Represents a set of hyperparameters to optimize.

Constructors

fun GoogleCloudMlV1__HyperparameterSpecArgs(algorithm: Output<GoogleCloudMlV1__HyperparameterSpecAlgorithm>? = null, enableTrialEarlyStopping: Output<Boolean>? = null, goal: Output<GoogleCloudMlV1__HyperparameterSpecGoal>, hyperparameterMetricTag: Output<String>? = null, maxFailedTrials: Output<Int>? = null, maxParallelTrials: Output<Int>? = null, maxTrials: Output<Int>? = null, params: Output<List<GoogleCloudMlV1__ParameterSpecArgs>>, resumePreviousJobId: Output<String>? = null)

Functions

toJava

open override fun toJava(): GoogleCloudMlV1__HyperparameterSpecArgs

Properties

algorithm

val algorithm: Output<GoogleCloudMlV1__HyperparameterSpecAlgorithm>? = null

Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.

enableTrialEarlyStopping

val enableTrialEarlyStopping: Output<Boolean>? = null

Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.

goal

val goal: Output<GoogleCloudMlV1__HyperparameterSpecGoal>

The type of goal to use for tuning. Available types are MAXIMIZE and MINIMIZE. Defaults to MAXIMIZE.

hyperparameterMetricTag

val hyperparameterMetricTag: Output<String>? = null

Optional. The TensorFlow summary tag name to use for optimizing trials. For current versions of TensorFlow, this tag name should exactly match what is shown in TensorBoard, including all scopes. For versions of TensorFlow prior to 0.12, this should be only the tag passed to tf.Summary. By default, "training/hptuning/metric" will be used.

maxFailedTrials

val maxFailedTrials: Output<Int>? = null

Optional. The number of failed trials that need to be seen before failing the hyperparameter tuning job. You can specify this field to override the default failing criteria for AI Platform hyperparameter tuning jobs. Defaults to zero, which means the service decides when a hyperparameter job should fail.

maxParallelTrials

val maxParallelTrials: Output<Int>? = null

Optional. The number of training trials to run concurrently. You can reduce the time it takes to perform hyperparameter tuning by adding trials in parallel. However, each trail only benefits from the information gained in completed trials. That means that a trial does not get access to the results of trials running at the same time, which could reduce the quality of the overall optimization. Each trial will use the same scale tier and machine types. Defaults to one.

maxTrials

val maxTrials: Output<Int>? = null

Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.

params

val params: Output<List<GoogleCloudMlV1__ParameterSpecArgs>>

The set of parameters to tune.

resumePreviousJobId

val resumePreviousJobId: Output<String>? = null

Optional. The prior hyperparameter tuning job id that users hope to continue with. The job id will be used to find the corresponding vizier study guid and resume the study.