Struct CreateModelInput

#[non_exhaustive]
pub struct CreateModelInput {
    pub model_name: Option<String>,
    pub primary_container: Option<ContainerDefinition>,
    pub containers: Option<Vec<ContainerDefinition>>,
    pub inference_execution_config: Option<InferenceExecutionConfig>,
    pub execution_role_arn: Option<String>,
    pub tags: Option<Vec<Tag>>,
    pub vpc_config: Option<VpcConfig>,
    pub enable_network_isolation: Option<bool>,
}

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.

model_name: Option<String>

The name of the new model.

primary_container: Option<ContainerDefinition>

The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

containers: Option<Vec<ContainerDefinition>>

Specifies the containers in the inference pipeline.

inference_execution_config: Option<InferenceExecutionConfig>

Specifies details of how containers in a multi-container endpoint are called.

execution_role_arn: Option<String>

The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see SageMaker Roles.

To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission.

tags: Option<Vec<Tag>>

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

vpc_config: Option<VpcConfig>

A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.

enable_network_isolation: Option<bool>

Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

Implementations

impl CreateModelInput

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

The name of the new model.

pub fn primary_container(&self) -> Option<&ContainerDefinition>

The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

pub fn containers(&self) -> &[ContainerDefinition]

Specifies the containers in the inference pipeline.

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

pub fn inference_execution_config(&self) -> Option<&InferenceExecutionConfig>

Specifies details of how containers in a multi-container endpoint are called.

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

The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see SageMaker Roles.

To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission.

pub fn tags(&self) -> &[Tag]

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

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

pub fn vpc_config(&self) -> Option<&VpcConfig>

A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.

pub fn enable_network_isolation(&self) -> Option<bool>

Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

impl CreateModelInput

pub fn builder() -> CreateModelInputBuilder

Creates a new builder-style object to manufacture CreateModelInput.

Trait Implementations

impl Clone for CreateModelInput

fn clone(&self) -> CreateModelInput

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CreateModelInput

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

Formats the value using the given formatter.

impl PartialEq for CreateModelInput

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