#[non_exhaustive]pub struct StartMlDataProcessingJobInput {Show 15 fields
pub id: Option<String>,
pub previous_data_processing_job_id: Option<String>,
pub input_data_s3_location: Option<String>,
pub processed_data_s3_location: Option<String>,
pub sagemaker_iam_role_arn: Option<String>,
pub neptune_iam_role_arn: Option<String>,
pub processing_instance_type: Option<String>,
pub processing_instance_volume_size_in_gb: Option<i32>,
pub processing_time_out_in_seconds: Option<i32>,
pub model_type: Option<String>,
pub config_file_name: Option<String>,
pub subnets: Option<Vec<String>>,
pub security_group_ids: Option<Vec<String>>,
pub volume_encryption_kms_key: Option<String>,
pub s3_output_encryption_kms_key: Option<String>,
}Fields (Non-exhaustive)§
This struct is marked as non-exhaustive
Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.id: Option<String>A unique identifier for the new job. The default is an autogenerated UUID.
previous_data_processing_job_id: Option<String>The job ID of a completed data processing job run on an earlier version of the data.
input_data_s3_location: Option<String>The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.
processed_data_s3_location: Option<String>The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.
sagemaker_iam_role_arn: 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.
neptune_iam_role_arn: 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.
processing_instance_type: 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.
processing_instance_volume_size_in_gb: 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.
processing_time_out_in_seconds: Option<i32>Timeout in seconds for the data processing job. The default is 86,400 (1 day).
model_type: 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.
config_file_name: 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.
subnets: Option<Vec<String>>The IDs of the subnets in the Neptune VPC. The default is None.
security_group_ids: Option<Vec<String>>The VPC security group IDs. The default is None.
volume_encryption_kms_key: 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.
s3_output_encryption_kms_key: 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.
Implementations§
Source§impl StartMlDataProcessingJobInput
impl StartMlDataProcessingJobInput
Sourcepub fn id(&self) -> Option<&str>
pub fn id(&self) -> Option<&str>
A unique identifier for the new job. The default is an autogenerated UUID.
Sourcepub fn previous_data_processing_job_id(&self) -> Option<&str>
pub fn previous_data_processing_job_id(&self) -> Option<&str>
The job ID of a completed data processing job run on an earlier version of the data.
Sourcepub fn input_data_s3_location(&self) -> Option<&str>
pub fn input_data_s3_location(&self) -> Option<&str>
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) -> Option<&str>
pub fn processed_data_s3_location(&self) -> Option<&str>
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) -> Option<&str>
pub fn sagemaker_iam_role_arn(&self) -> Option<&str>
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) -> Option<&str>
pub fn neptune_iam_role_arn(&self) -> Option<&str>
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) -> Option<&str>
pub fn processing_instance_type(&self) -> Option<&str>
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) -> Option<i32>
pub fn 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) -> Option<i32>
pub fn 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) -> Option<&str>
pub fn model_type(&self) -> Option<&str>
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) -> Option<&str>
pub fn config_file_name(&self) -> Option<&str>
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) -> &[String]
pub fn subnets(&self) -> &[String]
The IDs of the subnets in the Neptune VPC. The default is None.
If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .subnets.is_none().
Sourcepub fn security_group_ids(&self) -> &[String]
pub fn security_group_ids(&self) -> &[String]
The VPC security group IDs. The default is None.
If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .security_group_ids.is_none().
Sourcepub fn volume_encryption_kms_key(&self) -> Option<&str>
pub fn volume_encryption_kms_key(&self) -> Option<&str>
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) -> Option<&str>
pub fn s3_output_encryption_kms_key(&self) -> Option<&str>
The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.
Source§impl StartMlDataProcessingJobInput
impl StartMlDataProcessingJobInput
Sourcepub fn builder() -> StartMlDataProcessingJobInputBuilder
pub fn builder() -> StartMlDataProcessingJobInputBuilder
Creates a new builder-style object to manufacture StartMlDataProcessingJobInput.
Trait Implementations§
Source§impl Clone for StartMlDataProcessingJobInput
impl Clone for StartMlDataProcessingJobInput
Source§fn clone(&self) -> StartMlDataProcessingJobInput
fn clone(&self) -> StartMlDataProcessingJobInput
1.0.0 · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source. Read moreSource§impl PartialEq for StartMlDataProcessingJobInput
impl PartialEq for StartMlDataProcessingJobInput
Source§fn eq(&self, other: &StartMlDataProcessingJobInput) -> bool
fn eq(&self, other: &StartMlDataProcessingJobInput) -> bool
self and other values to be equal, and is used by ==.impl StructuralPartialEq for StartMlDataProcessingJobInput
Auto Trait Implementations§
impl Freeze for StartMlDataProcessingJobInput
impl RefUnwindSafe for StartMlDataProcessingJobInput
impl Send for StartMlDataProcessingJobInput
impl Sync for StartMlDataProcessingJobInput
impl Unpin for StartMlDataProcessingJobInput
impl UnwindSafe for StartMlDataProcessingJobInput
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