Struct ContainerDefinitionBuilder

#[non_exhaustive]
pub struct ContainerDefinitionBuilder { /* private fields */ }

A builder for ContainerDefinition.

Implementations

impl ContainerDefinitionBuilder

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

This parameter is ignored for models that contain only a PrimaryContainer.

When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

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

This parameter is ignored for models that contain only a PrimaryContainer.

When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

pub fn get_container_hostname(&self) -> &Option<String>

This parameter is ignored for models that contain only a PrimaryContainer.

When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.

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

The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. 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.

The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

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

The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. 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.

The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

pub fn get_image(&self) -> &Option<String>

The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. 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.

The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

pub fn image_config(self, input: ImageConfig) -> Self

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.

The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

pub fn set_image_config(self, input: Option<ImageConfig>) -> Self

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.

The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

pub fn get_image_config(&self) -> &Option<ImageConfig>

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.

The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating.

pub fn mode(self, input: ContainerMode) -> Self

Whether the container hosts a single model or multiple models.

pub fn set_mode(self, input: Option<ContainerMode>) -> Self

Whether the container hosts a single model or multiple models.

pub fn get_mode(&self) -> &Option<ContainerMode>

Whether the container hosts a single model or multiple models.

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

The 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 S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.

If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.

If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

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

The 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 S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.

If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.

If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

pub fn get_model_data_url(&self) -> &Option<String>

The 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 S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.

The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.

If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.

If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl.

pub fn model_data_source(self, input: ModelDataSource) -> Self

Specifies the location of ML model data to deploy.

Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.

pub fn set_model_data_source(self, input: Option<ModelDataSource>) -> Self

Specifies the location of ML model data to deploy.

Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.

pub fn get_model_data_source(&self) -> &Option<ModelDataSource>

Specifies the location of ML model data to deploy.

Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace.

pub fn additional_model_data_sources( self, input: AdditionalModelDataSource, ) -> Self

Appends an item to additional_model_data_sources.

To override the contents of this collection use set_additional_model_data_sources.

Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.

pub fn set_additional_model_data_sources( self, input: Option<Vec<AdditionalModelDataSource>>, ) -> Self

Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.

pub fn get_additional_model_data_sources( &self, ) -> &Option<Vec<AdditionalModelDataSource>>

Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.

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. Don't include any sensitive data in your environment variables.

The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.

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

The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables.

The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.

pub fn get_environment(&self) -> &Option<HashMap<String, String>>

The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables.

The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.

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

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

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

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

pub fn get_model_package_name(&self) -> &Option<String>

The name or Amazon Resource Name (ARN) of the model package to use to create the model.

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

The inference specification name in the model package version.

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

The inference specification name in the model package version.

pub fn get_inference_specification_name(&self) -> &Option<String>

The inference specification name in the model package version.

pub fn multi_model_config(self, input: MultiModelConfig) -> Self

Specifies additional configuration for multi-model endpoints.

pub fn set_multi_model_config(self, input: Option<MultiModelConfig>) -> Self

Specifies additional configuration for multi-model endpoints.

pub fn get_multi_model_config(&self) -> &Option<MultiModelConfig>

Specifies additional configuration for multi-model endpoints.

pub fn build(self) -> ContainerDefinition

Consumes the builder and constructs a ContainerDefinition.

Trait Implementations

impl Clone for ContainerDefinitionBuilder

fn clone(&self) -> ContainerDefinitionBuilder

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ContainerDefinitionBuilder

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

Formats the value using the given formatter.

impl Default for ContainerDefinitionBuilder

fn default() -> ContainerDefinitionBuilder

Returns the “default value” for a type.

impl PartialEq for ContainerDefinitionBuilder

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