
commonMain.aws.sdk.kotlin.services.neptunedata.model.CustomModelTransformParameters.kt Maven / Gradle / Ivy
// Code generated by smithy-kotlin-codegen. DO NOT EDIT!
package aws.sdk.kotlin.services.neptunedata.model
import aws.smithy.kotlin.runtime.SdkDsl
/**
* Contains custom model transform parameters. See [Use a trained model to generate new model artifacts](https://docs.aws.amazon.com/neptune/latest/userguide/machine-learning-model-transform.html).
*/
public class CustomModelTransformParameters private constructor(builder: Builder) {
/**
* The path to the Amazon S3 location where the Python module implementing your model is located. This must point to a valid existing Amazon S3 location that contains, at a minimum, a training script, a transform script, and a `model-hpo-configuration.json` file.
*/
public val sourceS3DirectoryPath: kotlin.String = requireNotNull(builder.sourceS3DirectoryPath) { "A non-null value must be provided for sourceS3DirectoryPath" }
/**
* The name of the entry point in your module of a script that should be run after the best model from the hyperparameter search has been identified, to compute the model artifacts necessary for model deployment. It should be able to run with no command-line arguments. The default is `transform.py`.
*/
public val transformEntryPointScript: kotlin.String? = builder.transformEntryPointScript
public companion object {
public operator fun invoke(block: Builder.() -> kotlin.Unit): aws.sdk.kotlin.services.neptunedata.model.CustomModelTransformParameters = Builder().apply(block).build()
}
override fun toString(): kotlin.String = buildString {
append("CustomModelTransformParameters(")
append("sourceS3DirectoryPath=$sourceS3DirectoryPath,")
append("transformEntryPointScript=$transformEntryPointScript")
append(")")
}
override fun hashCode(): kotlin.Int {
var result = sourceS3DirectoryPath.hashCode()
result = 31 * result + (transformEntryPointScript?.hashCode() ?: 0)
return result
}
override fun equals(other: kotlin.Any?): kotlin.Boolean {
if (this === other) return true
if (other == null || this::class != other::class) return false
other as CustomModelTransformParameters
if (sourceS3DirectoryPath != other.sourceS3DirectoryPath) return false
if (transformEntryPointScript != other.transformEntryPointScript) return false
return true
}
public inline fun copy(block: Builder.() -> kotlin.Unit = {}): aws.sdk.kotlin.services.neptunedata.model.CustomModelTransformParameters = Builder(this).apply(block).build()
@SdkDsl
public class Builder {
/**
* The path to the Amazon S3 location where the Python module implementing your model is located. This must point to a valid existing Amazon S3 location that contains, at a minimum, a training script, a transform script, and a `model-hpo-configuration.json` file.
*/
public var sourceS3DirectoryPath: kotlin.String? = null
/**
* The name of the entry point in your module of a script that should be run after the best model from the hyperparameter search has been identified, to compute the model artifacts necessary for model deployment. It should be able to run with no command-line arguments. The default is `transform.py`.
*/
public var transformEntryPointScript: kotlin.String? = null
@PublishedApi
internal constructor()
@PublishedApi
internal constructor(x: aws.sdk.kotlin.services.neptunedata.model.CustomModelTransformParameters) : this() {
this.sourceS3DirectoryPath = x.sourceS3DirectoryPath
this.transformEntryPointScript = x.transformEntryPointScript
}
@PublishedApi
internal fun build(): aws.sdk.kotlin.services.neptunedata.model.CustomModelTransformParameters = CustomModelTransformParameters(this)
internal fun correctErrors(): Builder {
if (sourceS3DirectoryPath == null) sourceS3DirectoryPath = ""
return this
}
}
}
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