Struct aws_sdk_sagemaker::model::algorithm_specification::Builder

pub struct Builder { /* private fields */ }

A builder for AlgorithmSpecification.

Implementations

impl Builder

pub fn training_image(self, input: impl Into<String>) -> Self

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 set_training_image(self, input: Option<String>) -> Self

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, input: impl Into<String>) -> Self

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 set_algorithm_name(self, input: Option<String>) -> Self

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, input: TrainingInputMode) -> Self

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 set_training_input_mode(self, input: Option<TrainingInputMode>) -> Self

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, input: MetricDefinition) -> Self

Appends an item to metric_definitions.

To override the contents of this collection use set_metric_definitions.

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.

pub fn set_metric_definitions(self, input: Option<Vec<MetricDefinition>>) -> Self

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.

pub fn enable_sage_maker_metrics_time_series(self, input: bool) -> Self

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 set_enable_sage_maker_metrics_time_series(
    self,
    input: Option<bool>
) -> Self

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, input: impl Into<String>) -> Self

Appends an item to container_entrypoint.

To override the contents of this collection use set_container_entrypoint.

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.

pub fn set_container_entrypoint(self, input: Option<Vec<String>>) -> Self

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.

pub fn container_arguments(self, input: impl Into<String>) -> Self

Appends an item to container_arguments.

To override the contents of this collection use set_container_arguments.

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

pub fn set_container_arguments(self, input: Option<Vec<String>>) -> Self

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

pub fn build(self) -> AlgorithmSpecification

Consumes the builder and constructs a AlgorithmSpecification.

Trait Implementations

impl Clone for Builder

fn clone(&self) -> Builder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Builder

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

Formats the value using the given formatter.

impl Default for Builder

fn default() -> Builder

Returns the “default value” for a type.

impl PartialEq<Builder> for Builder

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