Struct aws_sdk_sagemaker::types::TrainingSpecification

#[non_exhaustive]
pub struct TrainingSpecification {
    pub training_image: Option<String>,
    pub training_image_digest: Option<String>,
    pub supported_hyper_parameters: Option<Vec<HyperParameterSpecification>>,
    pub supported_training_instance_types: Option<Vec<TrainingInstanceType>>,
    pub supports_distributed_training: Option<bool>,
    pub metric_definitions: Option<Vec<MetricDefinition>>,
    pub training_channels: Option<Vec<ChannelSpecification>>,
    pub supported_tuning_job_objective_metrics: Option<Vec<HyperParameterTuningJobObjective>>,
    pub additional_s3_data_source: Option<AdditionalS3DataSource>,
}

Defines how the algorithm is used for a training job.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

training_image: Option<String>

The Amazon ECR registry path of the Docker image that contains the training algorithm.

training_image_digest: Option<String>

An MD5 hash of the training algorithm that identifies the Docker image used for training.

supported_hyper_parameters: Option<Vec<HyperParameterSpecification>>

A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>

supported_training_instance_types: Option<Vec<TrainingInstanceType>>

A list of the instance types that this algorithm can use for training.

supports_distributed_training: Option<bool>

Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.

metric_definitions: Option<Vec<MetricDefinition>>

A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

training_channels: Option<Vec<ChannelSpecification>>

A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

supported_tuning_job_objective_metrics: Option<Vec<HyperParameterTuningJobObjective>>

A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.

additional_s3_data_source: Option<AdditionalS3DataSource>

The additional data source used during the training job.

Implementations

impl TrainingSpecification

pub fn training_image(&self) -> Option<&str>

The Amazon ECR registry path of the Docker image that contains the training algorithm.

pub fn training_image_digest(&self) -> Option<&str>

An MD5 hash of the training algorithm that identifies the Docker image used for training.

pub fn supported_hyper_parameters(&self) -> &[HyperParameterSpecification]

A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .supported_hyper_parameters.is_none().

pub fn supported_training_instance_types(&self) -> &[TrainingInstanceType]

A list of the instance types that this algorithm can use for training.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .supported_training_instance_types.is_none().

pub fn supports_distributed_training(&self) -> Option<bool>

Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.

pub fn metric_definitions(&self) -> &[MetricDefinition]

A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .metric_definitions.is_none().

pub fn training_channels(&self) -> &[ChannelSpecification]

A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .training_channels.is_none().

pub fn supported_tuning_job_objective_metrics( &self, ) -> &[HyperParameterTuningJobObjective]

A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .supported_tuning_job_objective_metrics.is_none().

pub fn additional_s3_data_source(&self) -> Option<&AdditionalS3DataSource>

The additional data source used during the training job.

impl TrainingSpecification

pub fn builder() -> TrainingSpecificationBuilder

Creates a new builder-style object to manufacture TrainingSpecification.

Trait Implementations

impl Clone for TrainingSpecification

fn clone(&self) -> TrainingSpecification

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for TrainingSpecification

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

Formats the value using the given formatter.

impl PartialEq for TrainingSpecification

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

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

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

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for TrainingSpecification