Package software.amazon.awssdk.services.neptunedata.model

Interface StartMlModelTrainingJobRequest.Builder

All Superinterfaces:
AwsRequest.Builder, Buildable, CopyableBuilder<StartMlModelTrainingJobRequest.Builder,StartMlModelTrainingJobRequest>, NeptunedataRequest.Builder, SdkBuilder<StartMlModelTrainingJobRequest.Builder,StartMlModelTrainingJobRequest>, SdkPojo, SdkRequest.Builder
Enclosing class:
StartMlModelTrainingJobRequest
public static interface StartMlModelTrainingJobRequest.Builder extends NeptunedataRequest.Builder, SdkPojo, CopyableBuilder<StartMlModelTrainingJobRequest.Builder,StartMlModelTrainingJobRequest>

  • Method Details

    • id

      StartMlModelTrainingJobRequest.Builder id(String id)

      A unique identifier for the new job. The default is An autogenerated UUID.

      Parameters:
      id - A unique identifier for the new job. The default is An autogenerated UUID.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • previousModelTrainingJobId

      StartMlModelTrainingJobRequest.Builder previousModelTrainingJobId(String previousModelTrainingJobId)

      The job ID of a completed model-training job that you want to update incrementally based on updated data.

      Parameters:
      previousModelTrainingJobId - The job ID of a completed model-training job that you want to update incrementally based on updated data.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • dataProcessingJobId

      StartMlModelTrainingJobRequest.Builder dataProcessingJobId(String dataProcessingJobId)

      The job ID of the completed data-processing job that has created the data that the training will work with.

      Parameters:
      dataProcessingJobId - The job ID of the completed data-processing job that has created the data that the training will work with.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • trainModelS3Location

      StartMlModelTrainingJobRequest.Builder trainModelS3Location(String trainModelS3Location)

      The location in Amazon S3 where the model artifacts are to be stored.

      Parameters:
      trainModelS3Location - The location in Amazon S3 where the model artifacts are to be stored.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • sagemakerIamRoleArn

      StartMlModelTrainingJobRequest.Builder sagemakerIamRoleArn(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.

      Parameters:
      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.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • neptuneIamRoleArn

      StartMlModelTrainingJobRequest.Builder neptuneIamRoleArn(String neptuneIamRoleArn)

      The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

      Parameters:
      neptuneIamRoleArn - The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • baseProcessingInstanceType

      StartMlModelTrainingJobRequest.Builder baseProcessingInstanceType(String baseProcessingInstanceType)

      The type of ML instance used in preparing and managing training of ML models. This is a CPU instance chosen based on memory requirements for processing the training data and model.

      Parameters:
      baseProcessingInstanceType - The type of ML instance used in preparing and managing training of ML models. This is a CPU instance chosen based on memory requirements for processing the training data and model.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • trainingInstanceType

      StartMlModelTrainingJobRequest.Builder trainingInstanceType(String trainingInstanceType)

      The type of ML instance used for model training. All Neptune ML models support CPU, GPU, and multiGPU training. The default is ml.p3.2xlarge. Choosing the right instance type for training depends on the task type, graph size, and your budget.

      Parameters:
      trainingInstanceType - The type of ML instance used for model training. All Neptune ML models support CPU, GPU, and multiGPU training. The default is ml.p3.2xlarge. Choosing the right instance type for training depends on the task type, graph size, and your budget.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • trainingInstanceVolumeSizeInGB

      StartMlModelTrainingJobRequest.Builder trainingInstanceVolumeSizeInGB(Integer trainingInstanceVolumeSizeInGB)

      The disk volume size of the training instance. Both input data and the output model are stored on disk, so the volume size must be large enough to hold both data sets. The default is 0. If not specified or 0, Neptune ML selects a disk volume size based on the recommendation generated in the data processing step.

      Parameters:
      trainingInstanceVolumeSizeInGB - The disk volume size of the training instance. Both input data and the output model are stored on disk, so the volume size must be large enough to hold both data sets. The default is 0. If not specified or 0, Neptune ML selects a disk volume size based on the recommendation generated in the data processing step.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • trainingTimeOutInSeconds

      StartMlModelTrainingJobRequest.Builder trainingTimeOutInSeconds(Integer trainingTimeOutInSeconds)

      Timeout in seconds for the training job. The default is 86,400 (1 day).

      Parameters:
      trainingTimeOutInSeconds - Timeout in seconds for the training job. The default is 86,400 (1 day).
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • maxHPONumberOfTrainingJobs

      StartMlModelTrainingJobRequest.Builder maxHPONumberOfTrainingJobs(Integer maxHPONumberOfTrainingJobs)

      Maximum total number of training jobs to start for the hyperparameter tuning job. The default is 2. Neptune ML automatically tunes the hyperparameters of the machine learning model. To obtain a model that performs well, use at least 10 jobs (in other words, set maxHPONumberOfTrainingJobs to 10). In general, the more tuning runs, the better the results.

      Parameters:
      maxHPONumberOfTrainingJobs - Maximum total number of training jobs to start for the hyperparameter tuning job. The default is 2. Neptune ML automatically tunes the hyperparameters of the machine learning model. To obtain a model that performs well, use at least 10 jobs (in other words, set maxHPONumberOfTrainingJobs to 10). In general, the more tuning runs, the better the results.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • maxHPOParallelTrainingJobs

      StartMlModelTrainingJobRequest.Builder maxHPOParallelTrainingJobs(Integer maxHPOParallelTrainingJobs)

      Maximum number of parallel training jobs to start for the hyperparameter tuning job. The default is 2. The number of parallel jobs you can run is limited by the available resources on your training instance.

      Parameters:
      maxHPOParallelTrainingJobs - Maximum number of parallel training jobs to start for the hyperparameter tuning job. The default is 2. The number of parallel jobs you can run is limited by the available resources on your training instance.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • subnets

      StartMlModelTrainingJobRequest.Builder subnets(Collection<String> subnets)

      The IDs of the subnets in the Neptune VPC. The default is None.

      Parameters:
      subnets - The IDs of the subnets in the Neptune VPC. The default is None.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • subnets

      StartMlModelTrainingJobRequest.Builder subnets(String... subnets)

      The IDs of the subnets in the Neptune VPC. The default is None.

      Parameters:
      subnets - The IDs of the subnets in the Neptune VPC. The default is None.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • securityGroupIds

      StartMlModelTrainingJobRequest.Builder securityGroupIds(Collection<String> securityGroupIds)

      The VPC security group IDs. The default is None.

      Parameters:
      securityGroupIds - The VPC security group IDs. The default is None.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • securityGroupIds

      StartMlModelTrainingJobRequest.Builder securityGroupIds(String... securityGroupIds)

      The VPC security group IDs. The default is None.

      Parameters:
      securityGroupIds - The VPC security group IDs. The default is None.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • volumeEncryptionKMSKey

      StartMlModelTrainingJobRequest.Builder volumeEncryptionKMSKey(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. The default is None.

      Parameters:
      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. The default is None.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • s3OutputEncryptionKMSKey

      StartMlModelTrainingJobRequest.Builder s3OutputEncryptionKMSKey(String s3OutputEncryptionKMSKey)

      The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.

      Parameters:
      s3OutputEncryptionKMSKey - The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • enableManagedSpotTraining

      StartMlModelTrainingJobRequest.Builder enableManagedSpotTraining(Boolean enableManagedSpotTraining)

      Optimizes the cost of training machine-learning models by using Amazon Elastic Compute Cloud spot instances. The default is False.

      Parameters:
      enableManagedSpotTraining - Optimizes the cost of training machine-learning models by using Amazon Elastic Compute Cloud spot instances. The default is False.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • customModelTrainingParameters

      StartMlModelTrainingJobRequest.Builder customModelTrainingParameters(CustomModelTrainingParameters customModelTrainingParameters)

      The configuration for custom model training. This is a JSON object.

      Parameters:
      customModelTrainingParameters - The configuration for custom model training. This is a JSON object.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • customModelTrainingParameters

      default StartMlModelTrainingJobRequest.Builder customModelTrainingParameters(Consumer<CustomModelTrainingParameters.Builder> customModelTrainingParameters)

      The configuration for custom model training. This is a JSON object.

      This is a convenience method that creates an instance of the CustomModelTrainingParameters.Builder avoiding the need to create one manually via CustomModelTrainingParameters.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to customModelTrainingParameters(CustomModelTrainingParameters).

      Parameters:
      customModelTrainingParameters - a consumer that will call methods on CustomModelTrainingParameters.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • overrideConfiguration

      StartMlModelTrainingJobRequest.Builder overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration)
      Description copied from interface: AwsRequest.Builder
      Add an optional request override configuration.
      Specified by:
      overrideConfiguration in interface AwsRequest.Builder
      Parameters:
      overrideConfiguration - The override configuration.
      Returns:
      This object for method chaining.

    • overrideConfiguration

      StartMlModelTrainingJobRequest.Builder overrideConfiguration(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer)
      Description copied from interface: AwsRequest.Builder
      Add an optional request override configuration.
      Specified by:
      overrideConfiguration in interface AwsRequest.Builder
      Parameters:
      builderConsumer - A Consumer to which an empty AwsRequestOverrideConfiguration.Builder will be given.
      Returns:
      This object for method chaining.