Struct 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: Option<bool>,
    pub container_entrypoint: Option<Vec<String>>,
    pub container_arguments: Option<Vec<String>>,
    pub training_image_config: Option<TrainingImageConfig>,
}

Specifies the training algorithm to use in a CreateTrainingJob request.

SageMaker uses its own SageMaker account credentials to pull and access built-in algorithms so built-in algorithms are universally accessible across all Amazon Web Services accounts. As a result, built-in algorithms have standard, unrestricted access. You cannot restrict built-in algorithms using IAM roles. Use custom algorithms if you require specific access controls.

For more information about algorithms provided by 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 SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both registry/repository\[:tag\] and registry/repository\[@digest\] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

For more information, see the note in the AlgorithmName parameter description.

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.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage, and vice versa.

If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.

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. SageMaker publishes each metric to Amazon CloudWatch.

enable_sage_maker_metrics_time_series: Option<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 SageMaker built-in algorithms

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

  • Tensorflow (version >= 1.15)

  • MXNet (version >= 1.6)

  • PyTorch (version >= 1.3)

• You specify at least one MetricDefinition

container_entrypoint: Option<Vec<String>>

The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.

container_arguments: Option<Vec<String>>

The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.

training_image_config: Option<TrainingImageConfig>

The configuration to use an image from a private Docker registry for a training job.

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 SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both registry/repository\[:tag\] and registry/repository\[@digest\] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

For more information, see the note in the AlgorithmName parameter description.

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.

You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter.

Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage, and vice versa.

If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error.

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) -> &[MetricDefinition]

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

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 enable_sage_maker_metrics_time_series(&self) -> Option<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 SageMaker built-in algorithms

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

  • Tensorflow (version >= 1.15)

  • MXNet (version >= 1.6)

  • PyTorch (version >= 1.3)

• You specify at least one MetricDefinition

pub fn container_entrypoint(&self) -> &[String]

The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.

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

pub fn container_arguments(&self) -> &[String]

The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.

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

pub fn training_image_config(&self) -> Option<&TrainingImageConfig>

The configuration to use an image from a private Docker registry for a training job.

impl AlgorithmSpecification

pub fn builder() -> AlgorithmSpecificationBuilder

Creates a new builder-style object to manufacture AlgorithmSpecification.

Trait Implementations

impl Clone for AlgorithmSpecification

fn clone(&self) -> AlgorithmSpecification

Returns a duplicate 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 for AlgorithmSpecification

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for AlgorithmSpecification