Struct aws_sdk_sagemaker::model::AlgorithmSpecification

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

Specifies the training algorithm to use in a CreateTrainingJob request.

For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about using your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.

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. Amazon SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.

algorithm_name: Option<String>

The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage.

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.

metric_definitions: Option<Vec<MetricDefinition>>

A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.

enable_sage_maker_metrics_time_series: bool

To generate and save time-series metrics during training, set to true. The default is false and time-series metrics aren't generated except in the following cases:

• You use one of the Amazon SageMaker built-in algorithms

• You use one of the following Prebuilt Amazon SageMaker Docker Images:

  • Tensorflow (version >= 1.15)

  • MXNet (version >= 1.6)

  • PyTorch (version >= 1.3)

• You specify at least one MetricDefinition

Implementations

impl AlgorithmSpecification

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. Amazon 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 algorithm_name(&self) -> Option<&str>

The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace. If you specify a value for this parameter, you can't specify a value for TrainingImage.

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 metric_definitions(&self) -> Option<&[MetricDefinition]>

A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. Amazon SageMaker publishes each metric to Amazon CloudWatch.

pub fn enable_sage_maker_metrics_time_series(&self) -> bool

To generate and save time-series metrics during training, set to true. The default is false and time-series metrics aren't generated except in the following cases:

• You use one of the Amazon SageMaker built-in algorithms

• You use one of the following Prebuilt Amazon SageMaker Docker Images:

  • Tensorflow (version >= 1.15)

  • MXNet (version >= 1.6)

  • PyTorch (version >= 1.3)

• You specify at least one MetricDefinition

impl AlgorithmSpecification

pub fn builder() -> Builder

Creates a new builder-style object to manufacture AlgorithmSpecification

Trait Implementations

impl Clone for AlgorithmSpecification

fn clone(&self) -> AlgorithmSpecification

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for AlgorithmSpecification

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

Formats the value using the given formatter.

impl PartialEq<AlgorithmSpecification> for AlgorithmSpecification

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

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

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

This method tests for !=.

impl StructuralPartialEq for AlgorithmSpecification