Struct JupyterLabAppSettings

#[non_exhaustive]
pub struct JupyterLabAppSettings {
    pub default_resource_spec: Option<ResourceSpec>,
    pub custom_images: Option<Vec<CustomImage>>,
    pub lifecycle_config_arns: Option<Vec<String>>,
    pub code_repositories: Option<Vec<CodeRepository>>,
    pub app_lifecycle_management: Option<AppLifecycleManagement>,
    pub emr_settings: Option<EmrSettings>,
    pub built_in_lifecycle_config_arn: Option<String>,
}

The settings for the JupyterLab application.

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.

default_resource_spec: Option<ResourceSpec>

Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.

When both SageMakerImageVersionArn and SageMakerImageArn are passed, SageMakerImageVersionArn is used. Any updates to SageMakerImageArn will not take effect if SageMakerImageVersionArn already exists in the ResourceSpec because SageMakerImageVersionArn always takes precedence. To clear the value set for SageMakerImageVersionArn, pass None as the value.

custom_images: Option<Vec<CustomImage>>

A list of custom SageMaker images that are configured to run as a JupyterLab app.

lifecycle_config_arns: Option<Vec<String>>

The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.

code_repositories: Option<Vec<CodeRepository>>

A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.

app_lifecycle_management: Option<AppLifecycleManagement>

Indicates whether idle shutdown is activated for JupyterLab applications.

emr_settings: Option<EmrSettings>

The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.

built_in_lifecycle_config_arn: Option<String>

The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.

Implementations

impl JupyterLabAppSettings

pub fn default_resource_spec(&self) -> Option<&ResourceSpec>

Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on.

When both SageMakerImageVersionArn and SageMakerImageArn are passed, SageMakerImageVersionArn is used. Any updates to SageMakerImageArn will not take effect if SageMakerImageVersionArn already exists in the ResourceSpec because SageMakerImageVersionArn always takes precedence. To clear the value set for SageMakerImageVersionArn, pass None as the value.

pub fn custom_images(&self) -> &[CustomImage]

A list of custom SageMaker images that are configured to run as a JupyterLab app.

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

pub fn lifecycle_config_arns(&self) -> &[String]

The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.

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

pub fn code_repositories(&self) -> &[CodeRepository]

A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.

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

pub fn app_lifecycle_management(&self) -> Option<&AppLifecycleManagement>

Indicates whether idle shutdown is activated for JupyterLab applications.

pub fn emr_settings(&self) -> Option<&EmrSettings>

The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.

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

The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration.

impl JupyterLabAppSettings

pub fn builder() -> JupyterLabAppSettingsBuilder

Creates a new builder-style object to manufacture JupyterLabAppSettings.

Trait Implementations

impl Clone for JupyterLabAppSettings

fn clone(&self) -> JupyterLabAppSettings

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for JupyterLabAppSettings

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

Formats the value using the given formatter.

impl PartialEq for JupyterLabAppSettings

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