io.cloudshiftdev.awscdk.services.stepfunctions.tasks.SageMakerCreateTrainingJobProps.kt Maven / Gradle / Ivy
@file:Suppress("RedundantVisibilityModifier","RedundantUnitReturnType","RemoveRedundantQualifierName","unused","UnusedImport","ClassName","REDUNDANT_PROJECTION","DEPRECATION")
package io.cloudshiftdev.awscdk.services.stepfunctions.tasks
import io.cloudshiftdev.awscdk.Duration
import io.cloudshiftdev.awscdk.common.CdkDslMarker
import io.cloudshiftdev.awscdk.common.CdkObject
import io.cloudshiftdev.awscdk.common.CdkObjectWrappers
import io.cloudshiftdev.awscdk.services.iam.IRole
import io.cloudshiftdev.awscdk.services.stepfunctions.Credentials
import io.cloudshiftdev.awscdk.services.stepfunctions.IntegrationPattern
import io.cloudshiftdev.awscdk.services.stepfunctions.TaskStateBaseProps
import io.cloudshiftdev.awscdk.services.stepfunctions.Timeout
import kotlin.Any
import kotlin.Boolean
import kotlin.Deprecated
import kotlin.String
import kotlin.Unit
import kotlin.collections.List
import kotlin.collections.Map
import kotlin.jvm.JvmName
/**
* Properties for creating an Amazon SageMaker training job.
*
* Example:
*
* ```
* SageMakerCreateTrainingJob.Builder.create(this, "TrainSagemaker")
* .trainingJobName(JsonPath.stringAt("$.JobName"))
* .algorithmSpecification(AlgorithmSpecification.builder()
* .algorithmName("BlazingText")
* .trainingInputMode(InputMode.FILE)
* .build())
* .inputDataConfig(List.of(Channel.builder()
* .channelName("train")
* .dataSource(DataSource.builder()
* .s3DataSource(S3DataSource.builder()
* .s3DataType(S3DataType.S3_PREFIX)
* .s3Location(S3Location.fromJsonExpression("$.S3Bucket"))
* .build())
* .build())
* .build()))
* .outputDataConfig(OutputDataConfig.builder()
* .s3OutputLocation(S3Location.fromBucket(Bucket.fromBucketName(this, "Bucket", "mybucket"),
* "myoutputpath"))
* .build())
* .resourceConfig(ResourceConfig.builder()
* .instanceCount(1)
* .instanceType(new InstanceType(JsonPath.stringAt("$.InstanceType")))
* .volumeSize(Size.gibibytes(50))
* .build()) // optional: default is 1 instance of EC2 `M4.XLarge` with `10GB` volume
* .stoppingCondition(StoppingCondition.builder()
* .maxRuntime(Duration.hours(2))
* .build())
* .build();
* ```
*/
public interface SageMakerCreateTrainingJobProps : TaskStateBaseProps {
/**
* Identifies the training algorithm to use.
*/
public fun algorithmSpecification(): AlgorithmSpecification
/**
* Isolates the training container.
*
* No inbound or outbound network calls can be made to or from the training container.
*
* Default: false
*/
public fun enableNetworkIsolation(): Boolean? = unwrap(this).getEnableNetworkIsolation()
/**
* Environment variables to set in the Docker container.
*
* Default: - No environment variables
*/
public fun environment(): Map = unwrap(this).getEnvironment() ?: emptyMap()
/**
* Algorithm-specific parameters that influence the quality of the model.
*
* Set hyperparameters before you start the learning process.
* For a list of hyperparameters provided by Amazon SageMaker
*
* Default: - No hyperparameters
*
* [Documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html)
*/
public fun hyperparameters(): Map = unwrap(this).getHyperparameters() ?: emptyMap()
/**
* Describes the various datasets (e.g. train, validation, test) and the Amazon S3 location where
* stored.
*
* Default: - No inputDataConfig
*/
public fun inputDataConfig(): List =
unwrap(this).getInputDataConfig()?.map(Channel::wrap) ?: emptyList()
/**
* Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model
* training.
*/
public fun outputDataConfig(): OutputDataConfig
/**
* Specifies the resources, ML compute instances, and ML storage volumes to deploy for model
* training.
*
* Default: - 1 instance of EC2 `M4.XLarge` with `10GB` volume
*/
public fun resourceConfig(): ResourceConfig? =
unwrap(this).getResourceConfig()?.let(ResourceConfig::wrap)
/**
* Role for the Training Job.
*
* The role must be granted all necessary permissions for the SageMaker training job to
* be able to operate.
*
* See
* https://docs.aws.amazon.com/fr_fr/sagemaker/latest/dg/sagemaker-roles.html#sagemaker-roles-createtrainingjob-perms
*
* Default: - a role will be created.
*/
public fun role(): IRole? = unwrap(this).getRole()?.let(IRole::wrap)
/**
* Sets a time limit for training.
*
* Default: - max runtime of 1 hour
*/
public fun stoppingCondition(): StoppingCondition? =
unwrap(this).getStoppingCondition()?.let(StoppingCondition::wrap)
/**
* Tags to be applied to the train job.
*
* Default: - No tags
*/
public fun tags(): Map = unwrap(this).getTags() ?: emptyMap()
/**
* Training Job Name.
*/
public fun trainingJobName(): String
/**
* Specifies the VPC that you want your training job to connect to.
*
* Default: - No VPC
*/
public fun vpcConfig(): VpcConfig? = unwrap(this).getVpcConfig()?.let(VpcConfig::wrap)
/**
* A builder for [SageMakerCreateTrainingJobProps]
*/
@CdkDslMarker
public interface Builder {
/**
* @param algorithmSpecification Identifies the training algorithm to use.
*/
public fun algorithmSpecification(algorithmSpecification: AlgorithmSpecification)
/**
* @param algorithmSpecification Identifies the training algorithm to use.
*/
@kotlin.Suppress("INAPPLICABLE_JVM_NAME")
@JvmName("a62dd329b20c5fbc313a557f900ab6b5335cdee6740df83f0817f71cacd3949f")
public
fun algorithmSpecification(algorithmSpecification: AlgorithmSpecification.Builder.() -> Unit)
/**
* @param comment An optional description for this state.
*/
public fun comment(comment: String)
/**
* @param credentials Credentials for an IAM Role that the State Machine assumes for executing
* the task.
* This enables cross-account resource invocations.
*/
public fun credentials(credentials: Credentials)
/**
* @param credentials Credentials for an IAM Role that the State Machine assumes for executing
* the task.
* This enables cross-account resource invocations.
*/
@kotlin.Suppress("INAPPLICABLE_JVM_NAME")
@JvmName("a73eb1e212d218edba51fd72ee2f10c8a7e6b9872a42855d7d8336a02cc0c000")
public fun credentials(credentials: Credentials.Builder.() -> Unit)
/**
* @param enableNetworkIsolation Isolates the training container.
* No inbound or outbound network calls can be made to or from the training container.
*/
public fun enableNetworkIsolation(enableNetworkIsolation: Boolean)
/**
* @param environment Environment variables to set in the Docker container.
*/
public fun environment(environment: Map)
/**
* @param heartbeat Timeout for the heartbeat.
* @deprecated use `heartbeatTimeout`
*/
@Deprecated(message = "deprecated in CDK")
public fun heartbeat(heartbeat: Duration)
/**
* @param heartbeatTimeout Timeout for the heartbeat.
* [disable-awslint:duration-prop-type] is needed because all props interface in
* aws-stepfunctions-tasks extend this interface
*/
public fun heartbeatTimeout(heartbeatTimeout: Timeout)
/**
* @param hyperparameters Algorithm-specific parameters that influence the quality of the model.
* Set hyperparameters before you start the learning process.
* For a list of hyperparameters provided by Amazon SageMaker
*/
public fun hyperparameters(hyperparameters: Map)
/**
* @param inputDataConfig Describes the various datasets (e.g. train, validation, test) and the
* Amazon S3 location where stored.
*/
public fun inputDataConfig(inputDataConfig: List)
/**
* @param inputDataConfig Describes the various datasets (e.g. train, validation, test) and the
* Amazon S3 location where stored.
*/
public fun inputDataConfig(vararg inputDataConfig: Channel)
/**
* @param inputPath JSONPath expression to select part of the state to be the input to this
* state.
* May also be the special value JsonPath.DISCARD, which will cause the effective
* input to be the empty object {}.
*/
public fun inputPath(inputPath: String)
/**
* @param integrationPattern AWS Step Functions integrates with services directly in the Amazon
* States Language.
* You can control these AWS services using service integration patterns.
*
* Depending on the AWS Service, the Service Integration Pattern availability will vary.
*/
public fun integrationPattern(integrationPattern: IntegrationPattern)
/**
* @param outputDataConfig Identifies the Amazon S3 location where you want Amazon SageMaker to
* save the results of model training.
*/
public fun outputDataConfig(outputDataConfig: OutputDataConfig)
/**
* @param outputDataConfig Identifies the Amazon S3 location where you want Amazon SageMaker to
* save the results of model training.
*/
@kotlin.Suppress("INAPPLICABLE_JVM_NAME")
@JvmName("148eca6aaa48208c1417261edb06299348c8c6d0d05efce1c2a3f52e43b6701f")
public fun outputDataConfig(outputDataConfig: OutputDataConfig.Builder.() -> Unit)
/**
* @param outputPath JSONPath expression to select select a portion of the state output to pass
* to the next state.
* May also be the special value JsonPath.DISCARD, which will cause the effective
* output to be the empty object {}.
*/
public fun outputPath(outputPath: String)
/**
* @param resourceConfig Specifies the resources, ML compute instances, and ML storage volumes
* to deploy for model training.
*/
public fun resourceConfig(resourceConfig: ResourceConfig)
/**
* @param resourceConfig Specifies the resources, ML compute instances, and ML storage volumes
* to deploy for model training.
*/
@kotlin.Suppress("INAPPLICABLE_JVM_NAME")
@JvmName("8e10711462a1cb6a118e39cc90fdfc47378f3c83c3d0c8dc7f016aaed0842f9a")
public fun resourceConfig(resourceConfig: ResourceConfig.Builder.() -> Unit)
/**
* @param resultPath JSONPath expression to indicate where to inject the state's output.
* May also be the special value JsonPath.DISCARD, which will cause the state's
* input to become its output.
*/
public fun resultPath(resultPath: String)
/**
* @param resultSelector The JSON that will replace the state's raw result and become the
* effective result before ResultPath is applied.
* You can use ResultSelector to create a payload with values that are static
* or selected from the state's raw result.
*/
public fun resultSelector(resultSelector: Map)
/**
* @param role Role for the Training Job.
* The role must be granted all necessary permissions for the SageMaker training job to
* be able to operate.
*
* See
* https://docs.aws.amazon.com/fr_fr/sagemaker/latest/dg/sagemaker-roles.html#sagemaker-roles-createtrainingjob-perms
*/
public fun role(role: IRole)
/**
* @param stateName Optional name for this state.
*/
public fun stateName(stateName: String)
/**
* @param stoppingCondition Sets a time limit for training.
*/
public fun stoppingCondition(stoppingCondition: StoppingCondition)
/**
* @param stoppingCondition Sets a time limit for training.
*/
@kotlin.Suppress("INAPPLICABLE_JVM_NAME")
@JvmName("700b01f8c12666b4d5f4b3148047df4df0b6c4b7988d060e067ac919d8fab24f")
public fun stoppingCondition(stoppingCondition: StoppingCondition.Builder.() -> Unit)
/**
* @param tags Tags to be applied to the train job.
*/
public fun tags(tags: Map)
/**
* @param taskTimeout Timeout for the task.
* [disable-awslint:duration-prop-type] is needed because all props interface in
* aws-stepfunctions-tasks extend this interface
*/
public fun taskTimeout(taskTimeout: Timeout)
/**
* @param timeout Timeout for the task.
* @deprecated use `taskTimeout`
*/
@Deprecated(message = "deprecated in CDK")
public fun timeout(timeout: Duration)
/**
* @param trainingJobName Training Job Name.
*/
public fun trainingJobName(trainingJobName: String)
/**
* @param vpcConfig Specifies the VPC that you want your training job to connect to.
*/
public fun vpcConfig(vpcConfig: VpcConfig)
/**
* @param vpcConfig Specifies the VPC that you want your training job to connect to.
*/
@kotlin.Suppress("INAPPLICABLE_JVM_NAME")
@JvmName("446ace8c72d501f0e2e095a3266ee045c8ff9481fb95a964796d40a9583dfc38")
public fun vpcConfig(vpcConfig: VpcConfig.Builder.() -> Unit)
}
private class BuilderImpl : Builder {
private val cdkBuilder:
software.amazon.awscdk.services.stepfunctions.tasks.SageMakerCreateTrainingJobProps.Builder
=
software.amazon.awscdk.services.stepfunctions.tasks.SageMakerCreateTrainingJobProps.builder()
/**
* @param algorithmSpecification Identifies the training algorithm to use.
*/
override fun algorithmSpecification(algorithmSpecification: AlgorithmSpecification) {
cdkBuilder.algorithmSpecification(algorithmSpecification.let(AlgorithmSpecification.Companion::unwrap))
}
/**
* @param algorithmSpecification Identifies the training algorithm to use.
*/
@kotlin.Suppress("INAPPLICABLE_JVM_NAME")
@JvmName("a62dd329b20c5fbc313a557f900ab6b5335cdee6740df83f0817f71cacd3949f")
override
fun algorithmSpecification(algorithmSpecification: AlgorithmSpecification.Builder.() -> Unit):
Unit = algorithmSpecification(AlgorithmSpecification(algorithmSpecification))
/**
* @param comment An optional description for this state.
*/
override fun comment(comment: String) {
cdkBuilder.comment(comment)
}
/**
* @param credentials Credentials for an IAM Role that the State Machine assumes for executing
* the task.
* This enables cross-account resource invocations.
*/
override fun credentials(credentials: Credentials) {
cdkBuilder.credentials(credentials.let(Credentials.Companion::unwrap))
}
/**
* @param credentials Credentials for an IAM Role that the State Machine assumes for executing
* the task.
* This enables cross-account resource invocations.
*/
@kotlin.Suppress("INAPPLICABLE_JVM_NAME")
@JvmName("a73eb1e212d218edba51fd72ee2f10c8a7e6b9872a42855d7d8336a02cc0c000")
override fun credentials(credentials: Credentials.Builder.() -> Unit): Unit =
credentials(Credentials(credentials))
/**
* @param enableNetworkIsolation Isolates the training container.
* No inbound or outbound network calls can be made to or from the training container.
*/
override fun enableNetworkIsolation(enableNetworkIsolation: Boolean) {
cdkBuilder.enableNetworkIsolation(enableNetworkIsolation)
}
/**
* @param environment Environment variables to set in the Docker container.
*/
override fun environment(environment: Map) {
cdkBuilder.environment(environment)
}
/**
* @param heartbeat Timeout for the heartbeat.
* @deprecated use `heartbeatTimeout`
*/
@Deprecated(message = "deprecated in CDK")
override fun heartbeat(heartbeat: Duration) {
cdkBuilder.heartbeat(heartbeat.let(Duration.Companion::unwrap))
}
/**
* @param heartbeatTimeout Timeout for the heartbeat.
* [disable-awslint:duration-prop-type] is needed because all props interface in
* aws-stepfunctions-tasks extend this interface
*/
override fun heartbeatTimeout(heartbeatTimeout: Timeout) {
cdkBuilder.heartbeatTimeout(heartbeatTimeout.let(Timeout.Companion::unwrap))
}
/**
* @param hyperparameters Algorithm-specific parameters that influence the quality of the model.
* Set hyperparameters before you start the learning process.
* For a list of hyperparameters provided by Amazon SageMaker
*/
override fun hyperparameters(hyperparameters: Map) {
cdkBuilder.hyperparameters(hyperparameters.mapValues{CdkObjectWrappers.unwrap(it.value)})
}
/**
* @param inputDataConfig Describes the various datasets (e.g. train, validation, test) and the
* Amazon S3 location where stored.
*/
override fun inputDataConfig(inputDataConfig: List) {
cdkBuilder.inputDataConfig(inputDataConfig.map(Channel.Companion::unwrap))
}
/**
* @param inputDataConfig Describes the various datasets (e.g. train, validation, test) and the
* Amazon S3 location where stored.
*/
override fun inputDataConfig(vararg inputDataConfig: Channel): Unit =
inputDataConfig(inputDataConfig.toList())
/**
* @param inputPath JSONPath expression to select part of the state to be the input to this
* state.
* May also be the special value JsonPath.DISCARD, which will cause the effective
* input to be the empty object {}.
*/
override fun inputPath(inputPath: String) {
cdkBuilder.inputPath(inputPath)
}
/**
* @param integrationPattern AWS Step Functions integrates with services directly in the Amazon
* States Language.
* You can control these AWS services using service integration patterns.
*
* Depending on the AWS Service, the Service Integration Pattern availability will vary.
*/
override fun integrationPattern(integrationPattern: IntegrationPattern) {
cdkBuilder.integrationPattern(integrationPattern.let(IntegrationPattern.Companion::unwrap))
}
/**
* @param outputDataConfig Identifies the Amazon S3 location where you want Amazon SageMaker to
* save the results of model training.
*/
override fun outputDataConfig(outputDataConfig: OutputDataConfig) {
cdkBuilder.outputDataConfig(outputDataConfig.let(OutputDataConfig.Companion::unwrap))
}
/**
* @param outputDataConfig Identifies the Amazon S3 location where you want Amazon SageMaker to
* save the results of model training.
*/
@kotlin.Suppress("INAPPLICABLE_JVM_NAME")
@JvmName("148eca6aaa48208c1417261edb06299348c8c6d0d05efce1c2a3f52e43b6701f")
override fun outputDataConfig(outputDataConfig: OutputDataConfig.Builder.() -> Unit): Unit =
outputDataConfig(OutputDataConfig(outputDataConfig))
/**
* @param outputPath JSONPath expression to select select a portion of the state output to pass
* to the next state.
* May also be the special value JsonPath.DISCARD, which will cause the effective
* output to be the empty object {}.
*/
override fun outputPath(outputPath: String) {
cdkBuilder.outputPath(outputPath)
}
/**
* @param resourceConfig Specifies the resources, ML compute instances, and ML storage volumes
* to deploy for model training.
*/
override fun resourceConfig(resourceConfig: ResourceConfig) {
cdkBuilder.resourceConfig(resourceConfig.let(ResourceConfig.Companion::unwrap))
}
/**
* @param resourceConfig Specifies the resources, ML compute instances, and ML storage volumes
* to deploy for model training.
*/
@kotlin.Suppress("INAPPLICABLE_JVM_NAME")
@JvmName("8e10711462a1cb6a118e39cc90fdfc47378f3c83c3d0c8dc7f016aaed0842f9a")
override fun resourceConfig(resourceConfig: ResourceConfig.Builder.() -> Unit): Unit =
resourceConfig(ResourceConfig(resourceConfig))
/**
* @param resultPath JSONPath expression to indicate where to inject the state's output.
* May also be the special value JsonPath.DISCARD, which will cause the state's
* input to become its output.
*/
override fun resultPath(resultPath: String) {
cdkBuilder.resultPath(resultPath)
}
/**
* @param resultSelector The JSON that will replace the state's raw result and become the
* effective result before ResultPath is applied.
* You can use ResultSelector to create a payload with values that are static
* or selected from the state's raw result.
*/
override fun resultSelector(resultSelector: Map) {
cdkBuilder.resultSelector(resultSelector.mapValues{CdkObjectWrappers.unwrap(it.value)})
}
/**
* @param role Role for the Training Job.
* The role must be granted all necessary permissions for the SageMaker training job to
* be able to operate.
*
* See
* https://docs.aws.amazon.com/fr_fr/sagemaker/latest/dg/sagemaker-roles.html#sagemaker-roles-createtrainingjob-perms
*/
override fun role(role: IRole) {
cdkBuilder.role(role.let(IRole.Companion::unwrap))
}
/**
* @param stateName Optional name for this state.
*/
override fun stateName(stateName: String) {
cdkBuilder.stateName(stateName)
}
/**
* @param stoppingCondition Sets a time limit for training.
*/
override fun stoppingCondition(stoppingCondition: StoppingCondition) {
cdkBuilder.stoppingCondition(stoppingCondition.let(StoppingCondition.Companion::unwrap))
}
/**
* @param stoppingCondition Sets a time limit for training.
*/
@kotlin.Suppress("INAPPLICABLE_JVM_NAME")
@JvmName("700b01f8c12666b4d5f4b3148047df4df0b6c4b7988d060e067ac919d8fab24f")
override fun stoppingCondition(stoppingCondition: StoppingCondition.Builder.() -> Unit): Unit =
stoppingCondition(StoppingCondition(stoppingCondition))
/**
* @param tags Tags to be applied to the train job.
*/
override fun tags(tags: Map) {
cdkBuilder.tags(tags)
}
/**
* @param taskTimeout Timeout for the task.
* [disable-awslint:duration-prop-type] is needed because all props interface in
* aws-stepfunctions-tasks extend this interface
*/
override fun taskTimeout(taskTimeout: Timeout) {
cdkBuilder.taskTimeout(taskTimeout.let(Timeout.Companion::unwrap))
}
/**
* @param timeout Timeout for the task.
* @deprecated use `taskTimeout`
*/
@Deprecated(message = "deprecated in CDK")
override fun timeout(timeout: Duration) {
cdkBuilder.timeout(timeout.let(Duration.Companion::unwrap))
}
/**
* @param trainingJobName Training Job Name.
*/
override fun trainingJobName(trainingJobName: String) {
cdkBuilder.trainingJobName(trainingJobName)
}
/**
* @param vpcConfig Specifies the VPC that you want your training job to connect to.
*/
override fun vpcConfig(vpcConfig: VpcConfig) {
cdkBuilder.vpcConfig(vpcConfig.let(VpcConfig.Companion::unwrap))
}
/**
* @param vpcConfig Specifies the VPC that you want your training job to connect to.
*/
@kotlin.Suppress("INAPPLICABLE_JVM_NAME")
@JvmName("446ace8c72d501f0e2e095a3266ee045c8ff9481fb95a964796d40a9583dfc38")
override fun vpcConfig(vpcConfig: VpcConfig.Builder.() -> Unit): Unit =
vpcConfig(VpcConfig(vpcConfig))
public fun build():
software.amazon.awscdk.services.stepfunctions.tasks.SageMakerCreateTrainingJobProps =
cdkBuilder.build()
}
private class Wrapper(
cdkObject: software.amazon.awscdk.services.stepfunctions.tasks.SageMakerCreateTrainingJobProps,
) : CdkObject(cdkObject),
SageMakerCreateTrainingJobProps {
/**
* Identifies the training algorithm to use.
*/
override fun algorithmSpecification(): AlgorithmSpecification =
unwrap(this).getAlgorithmSpecification().let(AlgorithmSpecification::wrap)
/**
* An optional description for this state.
*
* Default: - No comment
*/
override fun comment(): String? = unwrap(this).getComment()
/**
* Credentials for an IAM Role that the State Machine assumes for executing the task.
*
* This enables cross-account resource invocations.
*
* Default: - None (Task is executed using the State Machine's execution role)
*
* [Documentation](https://docs.aws.amazon.com/step-functions/latest/dg/concepts-access-cross-acct-resources.html)
*/
override fun credentials(): Credentials? = unwrap(this).getCredentials()?.let(Credentials::wrap)
/**
* Isolates the training container.
*
* No inbound or outbound network calls can be made to or from the training container.
*
* Default: false
*/
override fun enableNetworkIsolation(): Boolean? = unwrap(this).getEnableNetworkIsolation()
/**
* Environment variables to set in the Docker container.
*
* Default: - No environment variables
*/
override fun environment(): Map = unwrap(this).getEnvironment() ?: emptyMap()
/**
* (deprecated) Timeout for the heartbeat.
*
* Default: - None
*
* @deprecated use `heartbeatTimeout`
*/
@Deprecated(message = "deprecated in CDK")
override fun heartbeat(): Duration? = unwrap(this).getHeartbeat()?.let(Duration::wrap)
/**
* Timeout for the heartbeat.
*
* [disable-awslint:duration-prop-type] is needed because all props interface in
* aws-stepfunctions-tasks extend this interface
*
* Default: - None
*/
override fun heartbeatTimeout(): Timeout? =
unwrap(this).getHeartbeatTimeout()?.let(Timeout::wrap)
/**
* Algorithm-specific parameters that influence the quality of the model.
*
* Set hyperparameters before you start the learning process.
* For a list of hyperparameters provided by Amazon SageMaker
*
* Default: - No hyperparameters
*
* [Documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html)
*/
override fun hyperparameters(): Map = unwrap(this).getHyperparameters() ?:
emptyMap()
/**
* Describes the various datasets (e.g. train, validation, test) and the Amazon S3 location
* where stored.
*
* Default: - No inputDataConfig
*/
override fun inputDataConfig(): List =
unwrap(this).getInputDataConfig()?.map(Channel::wrap) ?: emptyList()
/**
* JSONPath expression to select part of the state to be the input to this state.
*
* May also be the special value JsonPath.DISCARD, which will cause the effective
* input to be the empty object {}.
*
* Default: - The entire task input (JSON path '$')
*/
override fun inputPath(): String? = unwrap(this).getInputPath()
/**
* AWS Step Functions integrates with services directly in the Amazon States Language.
*
* You can control these AWS services using service integration patterns.
*
* Depending on the AWS Service, the Service Integration Pattern availability will vary.
*
* Default: - `IntegrationPattern.REQUEST_RESPONSE` for most tasks.
* `IntegrationPattern.RUN_JOB` for the following exceptions:
* `BatchSubmitJob`, `EmrAddStep`, `EmrCreateCluster`, `EmrTerminationCluster`, and
* `EmrContainersStartJobRun`.
*
* [Documentation](https://docs.aws.amazon.com/step-functions/latest/dg/connect-supported-services.html)
*/
override fun integrationPattern(): IntegrationPattern? =
unwrap(this).getIntegrationPattern()?.let(IntegrationPattern::wrap)
/**
* Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of
* model training.
*/
override fun outputDataConfig(): OutputDataConfig =
unwrap(this).getOutputDataConfig().let(OutputDataConfig::wrap)
/**
* JSONPath expression to select select a portion of the state output to pass to the next state.
*
* May also be the special value JsonPath.DISCARD, which will cause the effective
* output to be the empty object {}.
*
* Default: - The entire JSON node determined by the state input, the task result,
* and resultPath is passed to the next state (JSON path '$')
*/
override fun outputPath(): String? = unwrap(this).getOutputPath()
/**
* Specifies the resources, ML compute instances, and ML storage volumes to deploy for model
* training.
*
* Default: - 1 instance of EC2 `M4.XLarge` with `10GB` volume
*/
override fun resourceConfig(): ResourceConfig? =
unwrap(this).getResourceConfig()?.let(ResourceConfig::wrap)
/**
* JSONPath expression to indicate where to inject the state's output.
*
* May also be the special value JsonPath.DISCARD, which will cause the state's
* input to become its output.
*
* Default: - Replaces the entire input with the result (JSON path '$')
*/
override fun resultPath(): String? = unwrap(this).getResultPath()
/**
* The JSON that will replace the state's raw result and become the effective result before
* ResultPath is applied.
*
* You can use ResultSelector to create a payload with values that are static
* or selected from the state's raw result.
*
* Default: - None
*
* [Documentation](https://docs.aws.amazon.com/step-functions/latest/dg/input-output-inputpath-params.html#input-output-resultselector)
*/
override fun resultSelector(): Map = unwrap(this).getResultSelector() ?: emptyMap()
/**
* Role for the Training Job.
*
* The role must be granted all necessary permissions for the SageMaker training job to
* be able to operate.
*
* See
* https://docs.aws.amazon.com/fr_fr/sagemaker/latest/dg/sagemaker-roles.html#sagemaker-roles-createtrainingjob-perms
*
* Default: - a role will be created.
*/
override fun role(): IRole? = unwrap(this).getRole()?.let(IRole::wrap)
/**
* Optional name for this state.
*
* Default: - The construct ID will be used as state name
*/
override fun stateName(): String? = unwrap(this).getStateName()
/**
* Sets a time limit for training.
*
* Default: - max runtime of 1 hour
*/
override fun stoppingCondition(): StoppingCondition? =
unwrap(this).getStoppingCondition()?.let(StoppingCondition::wrap)
/**
* Tags to be applied to the train job.
*
* Default: - No tags
*/
override fun tags(): Map = unwrap(this).getTags() ?: emptyMap()
/**
* Timeout for the task.
*
* [disable-awslint:duration-prop-type] is needed because all props interface in
* aws-stepfunctions-tasks extend this interface
*
* Default: - None
*/
override fun taskTimeout(): Timeout? = unwrap(this).getTaskTimeout()?.let(Timeout::wrap)
/**
* (deprecated) Timeout for the task.
*
* Default: - None
*
* @deprecated use `taskTimeout`
*/
@Deprecated(message = "deprecated in CDK")
override fun timeout(): Duration? = unwrap(this).getTimeout()?.let(Duration::wrap)
/**
* Training Job Name.
*/
override fun trainingJobName(): String = unwrap(this).getTrainingJobName()
/**
* Specifies the VPC that you want your training job to connect to.
*
* Default: - No VPC
*/
override fun vpcConfig(): VpcConfig? = unwrap(this).getVpcConfig()?.let(VpcConfig::wrap)
}
public companion object {
public operator fun invoke(block: Builder.() -> Unit = {}): SageMakerCreateTrainingJobProps {
val builderImpl = BuilderImpl()
return Wrapper(builderImpl.apply(block).build())
}
internal
fun wrap(cdkObject: software.amazon.awscdk.services.stepfunctions.tasks.SageMakerCreateTrainingJobProps):
SageMakerCreateTrainingJobProps = CdkObjectWrappers.wrap(cdkObject) as?
SageMakerCreateTrainingJobProps ?: Wrapper(cdkObject)
internal fun unwrap(wrapped: SageMakerCreateTrainingJobProps):
software.amazon.awscdk.services.stepfunctions.tasks.SageMakerCreateTrainingJobProps =
(wrapped as CdkObject).cdkObject as
software.amazon.awscdk.services.stepfunctions.tasks.SageMakerCreateTrainingJobProps
}
}
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