Struct aws_sdk_neptunedata::operation::start_ml_data_processing_job::builders::StartMLDataProcessingJobFluentBuilder
source · pub struct StartMLDataProcessingJobFluentBuilder { /* private fields */ }
Expand description
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§
source§impl StartMLDataProcessingJobFluentBuilder
impl StartMLDataProcessingJobFluentBuilder
sourcepub fn as_input(&self) -> &StartMlDataProcessingJobInputBuilder
pub fn as_input(&self) -> &StartMlDataProcessingJobInputBuilder
Access the StartMLDataProcessingJob as a reference.
sourcepub async fn send(
self
) -> Result<StartMlDataProcessingJobOutput, SdkError<StartMLDataProcessingJobError, HttpResponse>>
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.
sourcepub fn customize(
self
) -> CustomizableOperation<StartMlDataProcessingJobOutput, StartMLDataProcessingJobError, Self>
pub fn customize( self ) -> CustomizableOperation<StartMlDataProcessingJobOutput, StartMLDataProcessingJobError, Self>
Consumes this builder, creating a customizable operation that can be modified before being sent.
sourcepub fn id(self, input: impl Into<String>) -> Self
pub fn id(self, input: impl Into<String>) -> Self
A unique identifier for the new job. The default is an autogenerated UUID.
sourcepub fn set_id(self, input: Option<String>) -> Self
pub fn set_id(self, input: Option<String>) -> Self
A unique identifier for the new job. The default is an autogenerated UUID.
sourcepub fn get_id(&self) -> &Option<String>
pub fn get_id(&self) -> &Option<String>
A unique identifier for the new job. The default is an autogenerated UUID.
sourcepub fn previous_data_processing_job_id(self, input: impl Into<String>) -> Self
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.
sourcepub fn set_previous_data_processing_job_id(self, input: Option<String>) -> Self
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.
sourcepub fn get_previous_data_processing_job_id(&self) -> &Option<String>
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.
sourcepub fn input_data_s3_location(self, input: impl Into<String>) -> Self
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.
sourcepub fn set_input_data_s3_location(self, input: Option<String>) -> Self
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.
sourcepub fn get_input_data_s3_location(&self) -> &Option<String>
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.
sourcepub fn processed_data_s3_location(self, input: impl Into<String>) -> Self
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.
sourcepub fn set_processed_data_s3_location(self, input: Option<String>) -> Self
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.
sourcepub fn get_processed_data_s3_location(&self) -> &Option<String>
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.
sourcepub fn sagemaker_iam_role_arn(self, input: impl Into<String>) -> Self
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.
sourcepub fn set_sagemaker_iam_role_arn(self, input: Option<String>) -> Self
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.
sourcepub fn get_sagemaker_iam_role_arn(&self) -> &Option<String>
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.
sourcepub fn neptune_iam_role_arn(self, input: impl Into<String>) -> Self
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.
sourcepub fn set_neptune_iam_role_arn(self, input: Option<String>) -> Self
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.
sourcepub fn get_neptune_iam_role_arn(&self) -> &Option<String>
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.
sourcepub fn processing_instance_type(self, input: impl Into<String>) -> Self
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.
sourcepub fn set_processing_instance_type(self, input: Option<String>) -> Self
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.
sourcepub fn get_processing_instance_type(&self) -> &Option<String>
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.
sourcepub fn processing_instance_volume_size_in_gb(self, input: i32) -> Self
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.
sourcepub fn set_processing_instance_volume_size_in_gb(
self,
input: Option<i32>
) -> Self
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.
sourcepub fn get_processing_instance_volume_size_in_gb(&self) -> &Option<i32>
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.
sourcepub fn processing_time_out_in_seconds(self, input: i32) -> Self
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).
sourcepub fn set_processing_time_out_in_seconds(self, input: Option<i32>) -> Self
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).
sourcepub fn get_processing_time_out_in_seconds(&self) -> &Option<i32>
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).
sourcepub fn model_type(self, input: impl Into<String>) -> Self
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.
sourcepub fn set_model_type(self, input: Option<String>) -> Self
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.
sourcepub fn get_model_type(&self) -> &Option<String>
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.
sourcepub fn config_file_name(self, input: impl Into<String>) -> Self
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
.
sourcepub fn set_config_file_name(self, input: Option<String>) -> Self
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
.
sourcepub fn get_config_file_name(&self) -> &Option<String>
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
.
sourcepub fn subnets(self, input: impl Into<String>) -> Self
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.
sourcepub fn set_subnets(self, input: Option<Vec<String>>) -> Self
pub fn set_subnets(self, input: Option<Vec<String>>) -> Self
The IDs of the subnets in the Neptune VPC. The default is None.
sourcepub fn get_subnets(&self) -> &Option<Vec<String>>
pub fn get_subnets(&self) -> &Option<Vec<String>>
The IDs of the subnets in the Neptune VPC. The default is None.
sourcepub fn security_group_ids(self, input: impl Into<String>) -> Self
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.
sourcepub fn set_security_group_ids(self, input: Option<Vec<String>>) -> Self
pub fn set_security_group_ids(self, input: Option<Vec<String>>) -> Self
The VPC security group IDs. The default is None.
sourcepub fn get_security_group_ids(&self) -> &Option<Vec<String>>
pub fn get_security_group_ids(&self) -> &Option<Vec<String>>
The VPC security group IDs. The default is None.
sourcepub fn volume_encryption_kms_key(self, input: impl Into<String>) -> Self
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.
sourcepub fn set_volume_encryption_kms_key(self, input: Option<String>) -> Self
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.
sourcepub fn get_volume_encryption_kms_key(&self) -> &Option<String>
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.
sourcepub fn s3_output_encryption_kms_key(self, input: impl Into<String>) -> Self
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.
sourcepub fn set_s3_output_encryption_kms_key(self, input: Option<String>) -> Self
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.
sourcepub fn get_s3_output_encryption_kms_key(&self) -> &Option<String>
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§
source§impl Clone for StartMLDataProcessingJobFluentBuilder
impl Clone for StartMLDataProcessingJobFluentBuilder
source§fn clone(&self) -> StartMLDataProcessingJobFluentBuilder
fn clone(&self) -> StartMLDataProcessingJobFluentBuilder
1.0.0 · source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source
. Read moreAuto Trait Implementations§
impl Freeze for StartMLDataProcessingJobFluentBuilder
impl !RefUnwindSafe for StartMLDataProcessingJobFluentBuilder
impl Send for StartMLDataProcessingJobFluentBuilder
impl Sync for StartMLDataProcessingJobFluentBuilder
impl Unpin for StartMLDataProcessingJobFluentBuilder
impl !UnwindSafe for StartMLDataProcessingJobFluentBuilder
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.
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