Struct aws_sdk_sagemaker::model::model_package_container_definition::Builder

pub struct Builder { /* private fields */ }

A builder for ModelPackageContainerDefinition.

Implementations

impl Builder

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

The DNS host name for the Docker container.

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

The DNS host name for the Docker container.

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

The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. 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 set_image(self, input: Option<String>) -> Self

The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored.

If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. 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 image_digest(self, input: impl Into<String>) -> Self

An MD5 hash of the training algorithm that identifies the Docker image used for training.

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

An MD5 hash of the training algorithm that identifies the Docker image used for training.

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

The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

The model artifacts must be in an S3 bucket that is in the same region as the model package.

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

The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

The model artifacts must be in an S3 bucket that is in the same region as the model package.

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

The Amazon Web Services Marketplace product ID of the model package.

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

The Amazon Web Services Marketplace product ID of the model package.

pub fn environment(self, k: impl Into<String>, v: impl Into<String>) -> Self

Adds a key-value pair to environment.

To override the contents of this collection use set_environment.

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

pub fn set_environment(self, input: Option<HashMap<String, String>>) -> Self

The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.

pub fn model_input(self, input: ModelInput) -> Self

A structure with Model Input details.

pub fn set_model_input(self, input: Option<ModelInput>) -> Self

A structure with Model Input details.

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

The machine learning framework of the model package container image.

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

The machine learning framework of the model package container image.

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

The framework version of the Model Package Container Image.

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

The framework version of the Model Package Container Image.

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

The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.

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

The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.

pub fn build(self) -> ModelPackageContainerDefinition

Consumes the builder and constructs a ModelPackageContainerDefinition.

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