#[non_exhaustive]pub struct TrainingSpecificationBuilder { /* private fields */ }
Expand description
A builder for TrainingSpecification
.
Implementations§
Source§impl TrainingSpecificationBuilder
impl TrainingSpecificationBuilder
Sourcepub fn training_image(self, input: impl Into<String>) -> Self
pub fn training_image(self, input: impl Into<String>) -> Self
The Amazon ECR registry path of the Docker image that contains the training algorithm.
This field is required.Sourcepub fn set_training_image(self, input: Option<String>) -> Self
pub fn set_training_image(self, input: Option<String>) -> Self
The Amazon ECR registry path of the Docker image that contains the training algorithm.
Sourcepub fn get_training_image(&self) -> &Option<String>
pub fn get_training_image(&self) -> &Option<String>
The Amazon ECR registry path of the Docker image that contains the training algorithm.
Sourcepub fn training_image_digest(self, input: impl Into<String>) -> Self
pub fn training_image_digest(self, input: impl Into<String>) -> Self
An MD5 hash of the training algorithm that identifies the Docker image used for training.
Sourcepub fn set_training_image_digest(self, input: Option<String>) -> Self
pub fn set_training_image_digest(self, input: Option<String>) -> Self
An MD5 hash of the training algorithm that identifies the Docker image used for training.
Sourcepub fn get_training_image_digest(&self) -> &Option<String>
pub fn get_training_image_digest(&self) -> &Option<String>
An MD5 hash of the training algorithm that identifies the Docker image used for training.
Sourcepub fn supported_hyper_parameters(
self,
input: HyperParameterSpecification,
) -> Self
pub fn supported_hyper_parameters( self, input: HyperParameterSpecification, ) -> Self
Appends an item to supported_hyper_parameters
.
To override the contents of this collection use set_supported_hyper_parameters
.
A list of the HyperParameterSpecification
objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>
Sourcepub fn set_supported_hyper_parameters(
self,
input: Option<Vec<HyperParameterSpecification>>,
) -> Self
pub fn set_supported_hyper_parameters( self, input: Option<Vec<HyperParameterSpecification>>, ) -> Self
A list of the HyperParameterSpecification
objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>
Sourcepub fn get_supported_hyper_parameters(
&self,
) -> &Option<Vec<HyperParameterSpecification>>
pub fn get_supported_hyper_parameters( &self, ) -> &Option<Vec<HyperParameterSpecification>>
A list of the HyperParameterSpecification
objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.>
Sourcepub fn supported_training_instance_types(
self,
input: TrainingInstanceType,
) -> Self
pub fn supported_training_instance_types( self, input: TrainingInstanceType, ) -> Self
Appends an item to supported_training_instance_types
.
To override the contents of this collection use set_supported_training_instance_types
.
A list of the instance types that this algorithm can use for training.
Sourcepub fn set_supported_training_instance_types(
self,
input: Option<Vec<TrainingInstanceType>>,
) -> Self
pub fn set_supported_training_instance_types( self, input: Option<Vec<TrainingInstanceType>>, ) -> Self
A list of the instance types that this algorithm can use for training.
Sourcepub fn get_supported_training_instance_types(
&self,
) -> &Option<Vec<TrainingInstanceType>>
pub fn get_supported_training_instance_types( &self, ) -> &Option<Vec<TrainingInstanceType>>
A list of the instance types that this algorithm can use for training.
Sourcepub fn supports_distributed_training(self, input: bool) -> Self
pub fn supports_distributed_training(self, input: bool) -> Self
Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
Sourcepub fn set_supports_distributed_training(self, input: Option<bool>) -> Self
pub fn set_supports_distributed_training(self, input: Option<bool>) -> Self
Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
Sourcepub fn get_supports_distributed_training(&self) -> &Option<bool>
pub fn get_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.
Sourcepub fn metric_definitions(self, input: MetricDefinition) -> Self
pub fn metric_definitions(self, input: MetricDefinition) -> Self
Appends an item to metric_definitions
.
To override the contents of this collection use set_metric_definitions
.
A list of MetricDefinition
objects, which are used for parsing metrics generated by the algorithm.
Sourcepub fn set_metric_definitions(
self,
input: Option<Vec<MetricDefinition>>,
) -> Self
pub fn set_metric_definitions( self, input: Option<Vec<MetricDefinition>>, ) -> Self
A list of MetricDefinition
objects, which are used for parsing metrics generated by the algorithm.
Sourcepub fn get_metric_definitions(&self) -> &Option<Vec<MetricDefinition>>
pub fn get_metric_definitions(&self) -> &Option<Vec<MetricDefinition>>
A list of MetricDefinition
objects, which are used for parsing metrics generated by the algorithm.
Sourcepub fn training_channels(self, input: ChannelSpecification) -> Self
pub fn training_channels(self, input: ChannelSpecification) -> Self
Appends an item to training_channels
.
To override the contents of this collection use set_training_channels
.
A list of ChannelSpecification
objects, which specify the input sources to be used by the algorithm.
Sourcepub fn set_training_channels(
self,
input: Option<Vec<ChannelSpecification>>,
) -> Self
pub fn set_training_channels( self, input: Option<Vec<ChannelSpecification>>, ) -> Self
A list of ChannelSpecification
objects, which specify the input sources to be used by the algorithm.
Sourcepub fn get_training_channels(&self) -> &Option<Vec<ChannelSpecification>>
pub fn get_training_channels(&self) -> &Option<Vec<ChannelSpecification>>
A list of ChannelSpecification
objects, which specify the input sources to be used by the algorithm.
Sourcepub fn supported_tuning_job_objective_metrics(
self,
input: HyperParameterTuningJobObjective,
) -> Self
pub fn supported_tuning_job_objective_metrics( self, input: HyperParameterTuningJobObjective, ) -> Self
Appends an item to supported_tuning_job_objective_metrics
.
To override the contents of this collection use set_supported_tuning_job_objective_metrics
.
A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
Sourcepub fn set_supported_tuning_job_objective_metrics(
self,
input: Option<Vec<HyperParameterTuningJobObjective>>,
) -> Self
pub fn set_supported_tuning_job_objective_metrics( self, input: Option<Vec<HyperParameterTuningJobObjective>>, ) -> Self
A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
Sourcepub fn get_supported_tuning_job_objective_metrics(
&self,
) -> &Option<Vec<HyperParameterTuningJobObjective>>
pub fn get_supported_tuning_job_objective_metrics( &self, ) -> &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.
Sourcepub fn additional_s3_data_source(self, input: AdditionalS3DataSource) -> Self
pub fn additional_s3_data_source(self, input: AdditionalS3DataSource) -> Self
The additional data source used during the training job.
Sourcepub fn set_additional_s3_data_source(
self,
input: Option<AdditionalS3DataSource>,
) -> Self
pub fn set_additional_s3_data_source( self, input: Option<AdditionalS3DataSource>, ) -> Self
The additional data source used during the training job.
Sourcepub fn get_additional_s3_data_source(&self) -> &Option<AdditionalS3DataSource>
pub fn get_additional_s3_data_source(&self) -> &Option<AdditionalS3DataSource>
The additional data source used during the training job.
Sourcepub fn build(self) -> TrainingSpecification
pub fn build(self) -> TrainingSpecification
Consumes the builder and constructs a TrainingSpecification
.
Trait Implementations§
Source§impl Clone for TrainingSpecificationBuilder
impl Clone for TrainingSpecificationBuilder
Source§fn clone(&self) -> TrainingSpecificationBuilder
fn clone(&self) -> TrainingSpecificationBuilder
1.0.0 · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source
. Read moreSource§impl Debug for TrainingSpecificationBuilder
impl Debug for TrainingSpecificationBuilder
Source§impl Default for TrainingSpecificationBuilder
impl Default for TrainingSpecificationBuilder
Source§fn default() -> TrainingSpecificationBuilder
fn default() -> TrainingSpecificationBuilder
Source§impl PartialEq for TrainingSpecificationBuilder
impl PartialEq for TrainingSpecificationBuilder
Source§fn eq(&self, other: &TrainingSpecificationBuilder) -> bool
fn eq(&self, other: &TrainingSpecificationBuilder) -> bool
self
and other
values to be equal, and is used by ==
.impl StructuralPartialEq for TrainingSpecificationBuilder
Auto Trait Implementations§
impl Freeze for TrainingSpecificationBuilder
impl RefUnwindSafe for TrainingSpecificationBuilder
impl Send for TrainingSpecificationBuilder
impl Sync for TrainingSpecificationBuilder
impl Unpin for TrainingSpecificationBuilder
impl UnwindSafe for TrainingSpecificationBuilder
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