Struct rusoto_glue::MLTransform

pub struct MLTransform {

    pub created_on: Option<f64>,
    pub description: Option<String>,
    pub evaluation_metrics: Option<EvaluationMetrics>,
    pub glue_version: Option<String>,
    pub input_record_tables: Option<Vec<GlueTable>>,
    pub label_count: Option<i64>,
    pub last_modified_on: Option<f64>,
    pub max_capacity: Option<f64>,
    pub max_retries: Option<i64>,
    pub name: Option<String>,
    pub number_of_workers: Option<i64>,
    pub parameters: Option<TransformParameters>,
    pub role: Option<String>,
    pub schema: Option<Vec<SchemaColumn>>,
    pub status: Option<String>,
    pub timeout: Option<i64>,
    pub transform_encryption: Option<TransformEncryption>,
    pub transform_id: Option<String>,
    pub worker_type: Option<String>,
}

A structure for a machine learning transform.

Fields

created_on: Option<f64>

A timestamp. The time and date that this machine learning transform was created.

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.

evaluation_metrics: Option<EvaluationMetrics>

An EvaluationMetrics object. Evaluation metrics provide an estimate of the quality of your machine learning 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.

input_record_tables: Option<Vec<GlueTable>>

A list of Glue table definitions used by the transform.

label_count: Option<i64>

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.

last_modified_on: Option<f64>

A timestamp. The last point in time when this machine learning transform was modified.

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 NumberOfWorkers or WorkerType is set, then MaxCapacity cannot be set.

• If MaxCapacity is set then neither NumberOfWorkers or WorkerType can be set.

• If WorkerType is set, then NumberOfWorkers is required (and vice versa).

• MaxCapacity and NumberOfWorkers must 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.

max_retries: Option<i64>

The maximum number of times to retry after an MLTaskRun of the machine learning transform fails.

name: Option<String>

A user-defined name for the machine learning transform. Names are not guaranteed unique and can be changed at any time.

number_of_workers: Option<i64>

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).

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).

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.

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.

status: Option<String>

The current status of the machine learning transform.

timeout: Option<i64>

The timeout in minutes of the machine learning transform.

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.

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.

worker_type: Option<String>

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 Standard worker type, each worker provides 4 vCPU, 16 GB of memory and a 50GB disk, and 2 executors per worker.

• For the G.1X worker type, each worker provides 4 vCPU, 16 GB of memory and a 64GB disk, and 1 executor per worker.

• For the G.2X worker 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 NumberOfWorkers or WorkerType is set, then MaxCapacity cannot be set.

• If MaxCapacity is set then neither NumberOfWorkers or WorkerType can be set.

• If WorkerType is set, then NumberOfWorkers is required (and vice versa).

• MaxCapacity and NumberOfWorkers must both be at least 1.

Trait Implementations

impl Clone for MLTransform

fn clone(&self) -> MLTransform

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for MLTransform

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

Formats the value using the given formatter.

impl Default for MLTransform

fn default() -> MLTransform

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for MLTransform

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.

impl PartialEq<MLTransform> for MLTransform

fn eq(&self, other: &MLTransform) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &MLTransform) -> bool

This method tests for !=.

impl StructuralPartialEq for MLTransform