Struct aws_sdk_neptunedata::operation::start_ml_model_transform_job::builders::StartMLModelTransformJobFluentBuilder

pub struct StartMLModelTransformJobFluentBuilder { /* private fields */ }

Fluent builder constructing a request to StartMLModelTransformJob.

Creates a new model transform job. See Use a trained model to generate new model artifacts.

When invoking this operation in a Neptune cluster that has IAM authentication enabled, the IAM user or role making the request must have a policy attached that allows the neptune-db:StartMLModelTransformJob IAM action in that cluster.

Implementations

impl StartMLModelTransformJobFluentBuilder

pub fn as_input(&self) -> &StartMlModelTransformJobInputBuilder

Access the StartMLModelTransformJob as a reference.

pub async fn send( self ) -> Result<StartMlModelTransformJobOutput, SdkError<StartMLModelTransformJobError, HttpResponse>>

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

pub fn customize( self ) -> CustomizableOperation<StartMlModelTransformJobOutput, StartMLModelTransformJobError, Self>

Consumes this builder, creating a customizable operation that can be modified before being sent.

pub fn id(self, input: impl Into<String>) -> Self

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

pub fn set_id(self, input: Option<String>) -> Self

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

pub fn get_id(&self) -> &Option<String>

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

pub fn data_processing_job_id(self, input: impl Into<String>) -> Self

The job ID of a completed data-processing job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

pub fn set_data_processing_job_id(self, input: Option<String>) -> Self

The job ID of a completed data-processing job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

pub fn get_data_processing_job_id(&self) -> &Option<String>

The job ID of a completed data-processing job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

pub fn ml_model_training_job_id(self, input: impl Into<String>) -> Self

The job ID of a completed model-training job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

pub fn set_ml_model_training_job_id(self, input: Option<String>) -> Self

The job ID of a completed model-training job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

pub fn get_ml_model_training_job_id(&self) -> &Option<String>

The job ID of a completed model-training job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

pub fn training_job_name(self, input: impl Into<String>) -> Self

The name of a completed SageMaker training job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

pub fn set_training_job_name(self, input: Option<String>) -> Self

The name of a completed SageMaker training job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

pub fn get_training_job_name(&self) -> &Option<String>

The name of a completed SageMaker training job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

pub fn model_transform_output_s3_location( self, input: impl Into<String> ) -> Self

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

pub fn set_model_transform_output_s3_location( self, input: Option<String> ) -> Self

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

pub fn get_model_transform_output_s3_location(&self) -> &Option<String>

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

pub fn sagemaker_iam_role_arn(self, input: impl Into<String>) -> Self

The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur.

pub fn set_sagemaker_iam_role_arn(self, input: Option<String>) -> Self

The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur.

pub fn get_sagemaker_iam_role_arn(&self) -> &Option<String>

The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur.

pub fn neptune_iam_role_arn(self, input: impl Into<String>) -> Self

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.

pub fn set_neptune_iam_role_arn(self, input: Option<String>) -> Self

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.

pub fn get_neptune_iam_role_arn(&self) -> &Option<String>

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.

pub fn custom_model_transform_parameters( self, input: CustomModelTransformParameters ) -> Self

Configuration information for a model transform using a custom model. The customModelTransformParameters object contains the following fields, which must have values compatible with the saved model parameters from the training job:

pub fn set_custom_model_transform_parameters( self, input: Option<CustomModelTransformParameters> ) -> Self

Configuration information for a model transform using a custom model. The customModelTransformParameters object contains the following fields, which must have values compatible with the saved model parameters from the training job:

pub fn get_custom_model_transform_parameters( &self ) -> &Option<CustomModelTransformParameters>

Configuration information for a model transform using a custom model. The customModelTransformParameters object contains the following fields, which must have values compatible with the saved model parameters from the training job:

pub fn base_processing_instance_type(self, input: impl Into<String>) -> Self

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

pub fn set_base_processing_instance_type(self, input: Option<String>) -> Self

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

pub fn get_base_processing_instance_type(&self) -> &Option<String>

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

pub fn base_processing_instance_volume_size_in_gb(self, input: i32) -> Self

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

pub fn set_base_processing_instance_volume_size_in_gb( self, input: Option<i32> ) -> Self

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

pub fn get_base_processing_instance_volume_size_in_gb(&self) -> &Option<i32>

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

pub fn subnets(self, input: impl Into<String>) -> Self

Appends an item to subnets.

To override the contents of this collection use set_subnets.

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

pub fn set_subnets(self, input: Option<Vec<String>>) -> Self

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

pub fn get_subnets(&self) -> &Option<Vec<String>>

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

pub fn security_group_ids(self, input: impl Into<String>) -> Self

Appends an item to securityGroupIds.

To override the contents of this collection use set_security_group_ids.

The VPC security group IDs. The default is None.

pub fn set_security_group_ids(self, input: Option<Vec<String>>) -> Self

The VPC security group IDs. The default is None.

pub fn get_security_group_ids(&self) -> &Option<Vec<String>>

The VPC security group IDs. The default is None.

pub fn volume_encryption_kms_key(self, input: impl Into<String>) -> Self

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.

pub fn set_volume_encryption_kms_key(self, input: Option<String>) -> Self

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.

pub fn get_volume_encryption_kms_key(&self) -> &Option<String>

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.

pub fn s3_output_encryption_kms_key(self, input: impl Into<String>) -> Self

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

pub fn set_s3_output_encryption_kms_key(self, input: Option<String>) -> Self

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

pub fn get_s3_output_encryption_kms_key(&self) -> &Option<String>

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

Trait Implementations

impl Clone for StartMLModelTransformJobFluentBuilder

fn clone(&self) -> StartMLModelTransformJobFluentBuilder

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for StartMLModelTransformJobFluentBuilder

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.