Struct StartMLDataProcessingJobFluentBuilder

pub struct StartMLDataProcessingJobFluentBuilder { /* private fields */ }

Fluent builder constructing a request to StartMLDataProcessingJob.

Creates a new Neptune ML data processing job for processing the graph data exported from Neptune for training. See The dataprocessing command.

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:StartMLModelDataProcessingJob IAM action in that cluster.

Implementations

impl StartMLDataProcessingJobFluentBuilder

pub fn as_input(&self) -> &StartMlDataProcessingJobInputBuilder

Access the StartMLDataProcessingJob as a reference.

pub async fn send( self, ) -> Result<StartMlDataProcessingJobOutput, SdkError<StartMLDataProcessingJobError, 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<StartMlDataProcessingJobOutput, StartMLDataProcessingJobError, 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 previous_data_processing_job_id(self, input: impl Into<String>) -> Self

The job ID of a completed data processing job run on an earlier version of the data.

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

The job ID of a completed data processing job run on an earlier version of the data.

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

The job ID of a completed data processing job run on an earlier version of the data.

pub fn input_data_s3_location(self, input: impl Into<String>) -> Self

The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.

pub fn set_input_data_s3_location(self, input: Option<String>) -> Self

The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.

pub fn get_input_data_s3_location(&self) -> &Option<String>

The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.

pub fn processed_data_s3_location(self, input: impl Into<String>) -> Self

The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.

pub fn set_processed_data_s3_location(self, input: Option<String>) -> Self

The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.

pub fn get_processed_data_s3_location(&self) -> &Option<String>

The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.

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 Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf. 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 Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf. 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 Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf. This must be listed in your DB cluster parameter group or an error will occur.

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

The type of ML instance used during data processing. Its memory should be large enough to hold the processed dataset. The default is the smallest ml.r5 type whose memory is ten times larger than the size of the exported graph data on disk.

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

The type of ML instance used during data processing. Its memory should be large enough to hold the processed dataset. The default is the smallest ml.r5 type whose memory is ten times larger than the size of the exported graph data on disk.

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

The type of ML instance used during data processing. Its memory should be large enough to hold the processed dataset. The default is the smallest ml.r5 type whose memory is ten times larger than the size of the exported graph data on disk.

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

The disk volume size of the processing instance. Both input data and processed data 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 chooses the volume size automatically based on the data size.

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

The disk volume size of the processing instance. Both input data and processed data 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 chooses the volume size automatically based on the data size.

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

The disk volume size of the processing instance. Both input data and processed data 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 chooses the volume size automatically based on the data size.

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

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

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

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

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

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

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

One of the two model types that Neptune ML currently supports: heterogeneous graph models (heterogeneous), and knowledge graph (kge). The default is none. If not specified, Neptune ML chooses the model type automatically based on the data.

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

One of the two model types that Neptune ML currently supports: heterogeneous graph models (heterogeneous), and knowledge graph (kge). The default is none. If not specified, Neptune ML chooses the model type automatically based on the data.

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

One of the two model types that Neptune ML currently supports: heterogeneous graph models (heterogeneous), and knowledge graph (kge). The default is none. If not specified, Neptune ML chooses the model type automatically based on the data.

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

A data specification file that describes how to load the exported graph data for training. The file is automatically generated by the Neptune export toolkit. The default is training-data-configuration.json.

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

A data specification file that describes how to load the exported graph data for training. The file is automatically generated by the Neptune export toolkit. The default is training-data-configuration.json.

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

A data specification file that describes how to load the exported graph data for training. The file is automatically generated by the Neptune export toolkit. The default is training-data-configuration.json.

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

Trait Implementations

impl Clone for StartMLDataProcessingJobFluentBuilder

fn clone(&self) -> StartMLDataProcessingJobFluentBuilder

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for StartMLDataProcessingJobFluentBuilder

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

Formats the value using the given formatter.