Uses of Class
com.amazonaws.services.neptunedata.model.StartMLModelTrainingJobRequest

Packages that use StartMLModelTrainingJobRequest

Package	Description
com.amazonaws.services.neptunedata	Neptune Data API
com.amazonaws.services.neptunedata.model

Uses of StartMLModelTrainingJobRequest in com.amazonaws.services.neptunedata

Methods in com.amazonaws.services.neptunedata with parameters of type StartMLModelTrainingJobRequest

Modifier and Type	Method and Description
StartMLModelTrainingJobResult	AmazonNeptunedataClient.startMLModelTrainingJob(StartMLModelTrainingJobRequest request) Creates a new Neptune ML model training job.
StartMLModelTrainingJobResult	AmazonNeptunedata.startMLModelTrainingJob(StartMLModelTrainingJobRequest startMLModelTrainingJobRequest) Creates a new Neptune ML model training job.
StartMLModelTrainingJobResult	AbstractAmazonNeptunedata.startMLModelTrainingJob(StartMLModelTrainingJobRequest request)
Future<StartMLModelTrainingJobResult>	AmazonNeptunedataAsyncClient.startMLModelTrainingJobAsync(StartMLModelTrainingJobRequest request)
Future<StartMLModelTrainingJobResult>	AmazonNeptunedataAsync.startMLModelTrainingJobAsync(StartMLModelTrainingJobRequest startMLModelTrainingJobRequest) Creates a new Neptune ML model training job.
Future<StartMLModelTrainingJobResult>	AbstractAmazonNeptunedataAsync.startMLModelTrainingJobAsync(StartMLModelTrainingJobRequest request)
Future<StartMLModelTrainingJobResult>	AmazonNeptunedataAsyncClient.startMLModelTrainingJobAsync(StartMLModelTrainingJobRequest request, AsyncHandler<StartMLModelTrainingJobRequest,StartMLModelTrainingJobResult> asyncHandler)
Future<StartMLModelTrainingJobResult>	AmazonNeptunedataAsync.startMLModelTrainingJobAsync(StartMLModelTrainingJobRequest startMLModelTrainingJobRequest, AsyncHandler<StartMLModelTrainingJobRequest,StartMLModelTrainingJobResult> asyncHandler) Creates a new Neptune ML model training job.
Future<StartMLModelTrainingJobResult>	AbstractAmazonNeptunedataAsync.startMLModelTrainingJobAsync(StartMLModelTrainingJobRequest request, AsyncHandler<StartMLModelTrainingJobRequest,StartMLModelTrainingJobResult> asyncHandler)

Method parameters in com.amazonaws.services.neptunedata with type arguments of type StartMLModelTrainingJobRequest

Modifier and Type	Method and Description
Future<StartMLModelTrainingJobResult>	AmazonNeptunedataAsyncClient.startMLModelTrainingJobAsync(StartMLModelTrainingJobRequest request, AsyncHandler<StartMLModelTrainingJobRequest,StartMLModelTrainingJobResult> asyncHandler)
Future<StartMLModelTrainingJobResult>	AmazonNeptunedataAsync.startMLModelTrainingJobAsync(StartMLModelTrainingJobRequest startMLModelTrainingJobRequest, AsyncHandler<StartMLModelTrainingJobRequest,StartMLModelTrainingJobResult> asyncHandler) Creates a new Neptune ML model training job.
Future<StartMLModelTrainingJobResult>	AbstractAmazonNeptunedataAsync.startMLModelTrainingJobAsync(StartMLModelTrainingJobRequest request, AsyncHandler<StartMLModelTrainingJobRequest,StartMLModelTrainingJobResult> asyncHandler)

Uses of StartMLModelTrainingJobRequest in com.amazonaws.services.neptunedata.model

Methods in com.amazonaws.services.neptunedata.model that return StartMLModelTrainingJobRequest

Modifier and Type	Method and Description
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.clone()
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withBaseProcessingInstanceType(String baseProcessingInstanceType) The type of ML instance used in preparing and managing training of ML models.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withCustomModelTrainingParameters(CustomModelTrainingParameters customModelTrainingParameters) The configuration for custom model training.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withDataProcessingJobId(String dataProcessingJobId) The job ID of the completed data-processing job that has created the data that the training will work with.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withEnableManagedSpotTraining(Boolean enableManagedSpotTraining) Optimizes the cost of training machine-learning models by using Amazon Elastic Compute Cloud spot instances.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withId(String id) A unique identifier for the new job.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withMaxHPONumberOfTrainingJobs(Integer maxHPONumberOfTrainingJobs) Maximum total number of training jobs to start for the hyperparameter tuning job.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withMaxHPOParallelTrainingJobs(Integer maxHPOParallelTrainingJobs) Maximum number of parallel training jobs to start for the hyperparameter tuning job.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withNeptuneIamRoleArn(String neptuneIamRoleArn) The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withPreviousModelTrainingJobId(String previousModelTrainingJobId) The job ID of a completed model-training job that you want to update incrementally based on updated data.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withS3OutputEncryptionKMSKey(String s3OutputEncryptionKMSKey) The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt the output of the processing job.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withSagemakerIamRoleArn(String sagemakerIamRoleArn) The ARN of an IAM role for SageMaker execution.This must be listed in your DB cluster parameter group or an error will occur.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withSecurityGroupIds(Collection<String> securityGroupIds) The VPC security group IDs.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withSecurityGroupIds(String... securityGroupIds) The VPC security group IDs.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withSubnets(Collection<String> subnets) The IDs of the subnets in the Neptune VPC.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withSubnets(String... subnets) The IDs of the subnets in the Neptune VPC.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withTrainingInstanceType(String trainingInstanceType) The type of ML instance used for model training.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withTrainingInstanceVolumeSizeInGB(Integer trainingInstanceVolumeSizeInGB) The disk volume size of the training instance.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withTrainingTimeOutInSeconds(Integer trainingTimeOutInSeconds) Timeout in seconds for the training job.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withTrainModelS3Location(String trainModelS3Location) The location in Amazon S3 where the model artifacts are to be stored.
StartMLModelTrainingJobRequest	StartMLModelTrainingJobRequest.withVolumeEncryptionKMSKey(String volumeEncryptionKMSKey) The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job.