Struct aws_sdk_sagemaker::types::HyperParameterAlgorithmSpecification

#[non_exhaustive]
pub struct HyperParameterAlgorithmSpecification {
    pub training_image: Option<String>,
    pub training_input_mode: Option<TrainingInputMode>,
    pub algorithm_name: Option<String>,
    pub metric_definitions: Option<Vec<MetricDefinition>>,
}

Specifies which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.

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 registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both registry/repository\[:tag\] and registry/repository\[@digest\] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

training_input_mode: Option<TrainingInputMode>

The training input mode that the algorithm supports. For more information about input modes, see Algorithms.

Pipe mode

If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

File mode

If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

FastFile mode

If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.

algorithm_name: Option<String>

The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage.

metric_definitions: Option<Vec<MetricDefinition>>

An array of MetricDefinition objects that specify the metrics that the algorithm emits.

Implementations

impl HyperParameterAlgorithmSpecification

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

The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both registry/repository\[:tag\] and registry/repository\[@digest\] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

pub fn training_input_mode(&self) -> Option<&TrainingInputMode>

The training input mode that the algorithm supports. For more information about input modes, see Algorithms.

Pipe mode

If an algorithm supports Pipe mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

File mode

If an algorithm supports File mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container.

You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.

For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.

FastFile mode

If an algorithm supports FastFile mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk.

FastFile mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided.

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

The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage.

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

An array of MetricDefinition objects that specify the metrics that the algorithm emits.

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().

impl HyperParameterAlgorithmSpecification

pub fn builder() -> HyperParameterAlgorithmSpecificationBuilder

Creates a new builder-style object to manufacture HyperParameterAlgorithmSpecification.

Trait Implementations

impl Clone for HyperParameterAlgorithmSpecification

fn clone(&self) -> HyperParameterAlgorithmSpecification

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for HyperParameterAlgorithmSpecification

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

Formats the value using the given formatter.

impl PartialEq for HyperParameterAlgorithmSpecification

fn eq(&self, other: &HyperParameterAlgorithmSpecification) -> 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 HyperParameterAlgorithmSpecification