Struct ContainerDefinition

#[non_exhaustive]
pub struct ContainerDefinition {
    pub container_hostname: Option<String>,
    pub image: Option<String>,
    pub image_config: Option<ImageConfig>,
    pub mode: Option<ContainerMode>,
    pub model_data_url: Option<String>,
    pub model_data_source: Option<ModelDataSource>,
    pub additional_model_data_sources: Option<Vec<AdditionalModelDataSource>>,
    pub environment: Option<HashMap<String, String>>,
    pub model_package_name: Option<String>,
    pub inference_specification_name: Option<String>,
    pub multi_model_config: Option<MultiModelConfig>,
}

Describes the container, as part of model definition.

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.

container_hostname: 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.

image: 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.

image_config: 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.

mode: Option<ContainerMode>

Whether the container hosts a single model or multiple models.

model_data_url: 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.

model_data_source: 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.

additional_model_data_sources: 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.

environment: 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.

model_package_name: Option<String>

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

inference_specification_name: Option<String>

The inference specification name in the model package version.

multi_model_config: Option<MultiModelConfig>

Specifies additional configuration for multi-model endpoints.

Implementations

impl ContainerDefinition

pub fn container_hostname(&self) -> Option<&str>

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) -> Option<&str>

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) -> 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) -> Option<&ContainerMode>

Whether the container hosts a single model or multiple models.

pub fn model_data_url(&self) -> Option<&str>

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) -> 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) -> &[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.

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

pub fn 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) -> Option<&str>

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

pub fn inference_specification_name(&self) -> Option<&str>

The inference specification name in the model package version.

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

Specifies additional configuration for multi-model endpoints.

impl ContainerDefinition

pub fn builder() -> ContainerDefinitionBuilder

Creates a new builder-style object to manufacture ContainerDefinition.

Trait Implementations

impl Clone for ContainerDefinition

fn clone(&self) -> ContainerDefinition

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ContainerDefinition

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

Formats the value using the given formatter.

impl PartialEq for ContainerDefinition

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