#[non_exhaustive]pub struct MlTransform {Show 19 fields
pub transform_id: Option<String>,
pub name: Option<String>,
pub description: Option<String>,
pub status: Option<TransformStatusType>,
pub created_on: Option<DateTime>,
pub last_modified_on: Option<DateTime>,
pub input_record_tables: Option<Vec<GlueTable>>,
pub parameters: Option<TransformParameters>,
pub evaluation_metrics: Option<EvaluationMetrics>,
pub label_count: i32,
pub schema: Option<Vec<SchemaColumn>>,
pub role: Option<String>,
pub glue_version: Option<String>,
pub max_capacity: Option<f64>,
pub worker_type: Option<WorkerType>,
pub number_of_workers: Option<i32>,
pub timeout: Option<i32>,
pub max_retries: Option<i32>,
pub transform_encryption: Option<TransformEncryption>,
}Expand description
A structure for a machine learning transform.
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.transform_id: Option<String>The unique transform ID that is generated for the machine learning transform. The ID is guaranteed to be unique and does not change.
name: Option<String>A user-defined name for the machine learning transform. Names are not guaranteed unique and can be changed at any time.
description: Option<String>A user-defined, long-form description text for the machine learning transform. Descriptions are not guaranteed to be unique and can be changed at any time.
status: Option<TransformStatusType>The current status of the machine learning transform.
created_on: Option<DateTime>A timestamp. The time and date that this machine learning transform was created.
last_modified_on: Option<DateTime>A timestamp. The last point in time when this machine learning transform was modified.
input_record_tables: Option<Vec<GlueTable>>A list of Glue table definitions used by the transform.
parameters: Option<TransformParameters>A TransformParameters object. You can use parameters to tune (customize) the behavior of the machine learning transform by specifying what data it learns from and your preference on various tradeoffs (such as precious vs. recall, or accuracy vs. cost).
evaluation_metrics: Option<EvaluationMetrics>An EvaluationMetrics object. Evaluation metrics provide an estimate of the quality of your machine learning transform.
label_count: i32A count identifier for the labeling files generated by Glue for this transform. As you create a better transform, you can iteratively download, label, and upload the labeling file.
schema: Option<Vec<SchemaColumn>>A map of key-value pairs representing the columns and data types that this transform can run against. Has an upper bound of 100 columns.
role: Option<String>The name or Amazon Resource Name (ARN) of the IAM role with the required permissions. The required permissions include both Glue service role permissions to Glue resources, and Amazon S3 permissions required by the transform.
-
This role needs Glue service role permissions to allow access to resources in Glue. See Attach a Policy to IAM Users That Access Glue.
-
This role needs permission to your Amazon Simple Storage Service (Amazon S3) sources, targets, temporary directory, scripts, and any libraries used by the task run for this transform.
glue_version: Option<String>This value determines which version of Glue this machine learning transform is compatible with. Glue 1.0 is recommended for most customers. If the value is not set, the Glue compatibility defaults to Glue 0.9. For more information, see Glue Versions in the developer guide.
max_capacity: Option<f64>The number of Glue data processing units (DPUs) that are allocated to task runs for this transform. You can allocate from 2 to 100 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the Glue pricing page.
MaxCapacity is a mutually exclusive option with NumberOfWorkers and WorkerType.
-
If either
NumberOfWorkersorWorkerTypeis set, thenMaxCapacitycannot be set. -
If
MaxCapacityis set then neitherNumberOfWorkersorWorkerTypecan be set. -
If
WorkerTypeis set, thenNumberOfWorkersis required (and vice versa). -
MaxCapacityandNumberOfWorkersmust both be at least 1.
When the WorkerType field is set to a value other than Standard, the MaxCapacity field is set automatically and becomes read-only.
worker_type: Option<WorkerType>The type of predefined worker that is allocated when a task of this transform runs. Accepts a value of Standard, G.1X, or G.2X.
-
For the
Standardworker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. -
For the
G.1Xworker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker. -
For the
G.2Xworker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker.
MaxCapacity is a mutually exclusive option with NumberOfWorkers and WorkerType.
-
If either
NumberOfWorkersorWorkerTypeis set, thenMaxCapacitycannot be set. -
If
MaxCapacityis set then neitherNumberOfWorkersorWorkerTypecan be set. -
If
WorkerTypeis set, thenNumberOfWorkersis required (and vice versa). -
MaxCapacityandNumberOfWorkersmust both be at least 1.
number_of_workers: Option<i32>The number of workers of a defined workerType that are allocated when a task of the transform runs.
If WorkerType is set, then NumberOfWorkers is required (and vice versa).
timeout: Option<i32>The timeout in minutes of the machine learning transform.
max_retries: Option<i32>The maximum number of times to retry after an MLTaskRun of the machine learning transform fails.
transform_encryption: Option<TransformEncryption>The encryption-at-rest settings of the transform that apply to accessing user data. Machine learning transforms can access user data encrypted in Amazon S3 using KMS.
Implementations§
Source§impl MlTransform
impl MlTransform
Sourcepub fn transform_id(&self) -> Option<&str>
pub fn transform_id(&self) -> Option<&str>
The unique transform ID that is generated for the machine learning transform. The ID is guaranteed to be unique and does not change.
Sourcepub fn name(&self) -> Option<&str>
pub fn name(&self) -> Option<&str>
A user-defined name for the machine learning transform. Names are not guaranteed unique and can be changed at any time.
Sourcepub fn description(&self) -> Option<&str>
pub fn description(&self) -> Option<&str>
A user-defined, long-form description text for the machine learning transform. Descriptions are not guaranteed to be unique and can be changed at any time.
Sourcepub fn status(&self) -> Option<&TransformStatusType>
pub fn status(&self) -> Option<&TransformStatusType>
The current status of the machine learning transform.
Sourcepub fn created_on(&self) -> Option<&DateTime>
pub fn created_on(&self) -> Option<&DateTime>
A timestamp. The time and date that this machine learning transform was created.
Sourcepub fn last_modified_on(&self) -> Option<&DateTime>
pub fn last_modified_on(&self) -> Option<&DateTime>
A timestamp. The last point in time when this machine learning transform was modified.
Sourcepub fn input_record_tables(&self) -> &[GlueTable]
pub fn input_record_tables(&self) -> &[GlueTable]
A list of Glue table definitions used by the transform.
If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .input_record_tables.is_none().
Sourcepub fn parameters(&self) -> Option<&TransformParameters>
pub fn parameters(&self) -> Option<&TransformParameters>
A TransformParameters object. You can use parameters to tune (customize) the behavior of the machine learning transform by specifying what data it learns from and your preference on various tradeoffs (such as precious vs. recall, or accuracy vs. cost).
Sourcepub fn evaluation_metrics(&self) -> Option<&EvaluationMetrics>
pub fn evaluation_metrics(&self) -> Option<&EvaluationMetrics>
An EvaluationMetrics object. Evaluation metrics provide an estimate of the quality of your machine learning transform.
Sourcepub fn label_count(&self) -> i32
pub fn label_count(&self) -> i32
A count identifier for the labeling files generated by Glue for this transform. As you create a better transform, you can iteratively download, label, and upload the labeling file.
Sourcepub fn schema(&self) -> &[SchemaColumn]
pub fn schema(&self) -> &[SchemaColumn]
A map of key-value pairs representing the columns and data types that this transform can run against. Has an upper bound of 100 columns.
If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .schema.is_none().
Sourcepub fn role(&self) -> Option<&str>
pub fn role(&self) -> Option<&str>
The name or Amazon Resource Name (ARN) of the IAM role with the required permissions. The required permissions include both Glue service role permissions to Glue resources, and Amazon S3 permissions required by the transform.
-
This role needs Glue service role permissions to allow access to resources in Glue. See Attach a Policy to IAM Users That Access Glue.
-
This role needs permission to your Amazon Simple Storage Service (Amazon S3) sources, targets, temporary directory, scripts, and any libraries used by the task run for this transform.
Sourcepub fn glue_version(&self) -> Option<&str>
pub fn glue_version(&self) -> Option<&str>
This value determines which version of Glue this machine learning transform is compatible with. Glue 1.0 is recommended for most customers. If the value is not set, the Glue compatibility defaults to Glue 0.9. For more information, see Glue Versions in the developer guide.
Sourcepub fn max_capacity(&self) -> Option<f64>
pub fn max_capacity(&self) -> Option<f64>
The number of Glue data processing units (DPUs) that are allocated to task runs for this transform. You can allocate from 2 to 100 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the Glue pricing page.
MaxCapacity is a mutually exclusive option with NumberOfWorkers and WorkerType.
-
If either
NumberOfWorkersorWorkerTypeis set, thenMaxCapacitycannot be set. -
If
MaxCapacityis set then neitherNumberOfWorkersorWorkerTypecan be set. -
If
WorkerTypeis set, thenNumberOfWorkersis required (and vice versa). -
MaxCapacityandNumberOfWorkersmust both be at least 1.
When the WorkerType field is set to a value other than Standard, the MaxCapacity field is set automatically and becomes read-only.
Sourcepub fn worker_type(&self) -> Option<&WorkerType>
pub fn worker_type(&self) -> Option<&WorkerType>
The type of predefined worker that is allocated when a task of this transform runs. Accepts a value of Standard, G.1X, or G.2X.
-
For the
Standardworker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker. -
For the
G.1Xworker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker. -
For the
G.2Xworker type, each worker provides 8 vCPU, 32 GB of memory and a 128GB disk, and 1 executor per worker.
MaxCapacity is a mutually exclusive option with NumberOfWorkers and WorkerType.
-
If either
NumberOfWorkersorWorkerTypeis set, thenMaxCapacitycannot be set. -
If
MaxCapacityis set then neitherNumberOfWorkersorWorkerTypecan be set. -
If
WorkerTypeis set, thenNumberOfWorkersis required (and vice versa). -
MaxCapacityandNumberOfWorkersmust both be at least 1.
Sourcepub fn number_of_workers(&self) -> Option<i32>
pub fn number_of_workers(&self) -> Option<i32>
The number of workers of a defined workerType that are allocated when a task of the transform runs.
If WorkerType is set, then NumberOfWorkers is required (and vice versa).
Sourcepub fn max_retries(&self) -> Option<i32>
pub fn max_retries(&self) -> Option<i32>
The maximum number of times to retry after an MLTaskRun of the machine learning transform fails.
Sourcepub fn transform_encryption(&self) -> Option<&TransformEncryption>
pub fn transform_encryption(&self) -> Option<&TransformEncryption>
The encryption-at-rest settings of the transform that apply to accessing user data. Machine learning transforms can access user data encrypted in Amazon S3 using KMS.
Source§impl MlTransform
impl MlTransform
Sourcepub fn builder() -> MlTransformBuilder
pub fn builder() -> MlTransformBuilder
Creates a new builder-style object to manufacture MlTransform.
Trait Implementations§
Source§impl Clone for MlTransform
impl Clone for MlTransform
Source§fn clone(&self) -> MlTransform
fn clone(&self) -> MlTransform
1.0.0 · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source. Read moreSource§impl Debug for MlTransform
impl Debug for MlTransform
Source§impl PartialEq for MlTransform
impl PartialEq for MlTransform
impl StructuralPartialEq for MlTransform
Auto Trait Implementations§
impl Freeze for MlTransform
impl RefUnwindSafe for MlTransform
impl Send for MlTransform
impl Sync for MlTransform
impl Unpin for MlTransform
impl UnwindSafe for MlTransform
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