Struct aws_sdk_sagemaker::input::CreateNotebookInstanceInput

#[non_exhaustive]
pub struct CreateNotebookInstanceInput {

    pub notebook_instance_name: Option<String>,
    pub instance_type: Option<InstanceType>,
    pub subnet_id: Option<String>,
    pub security_group_ids: Option<Vec<String>>,
    pub role_arn: Option<String>,
    pub kms_key_id: Option<String>,
    pub tags: Option<Vec<Tag>>,
    pub lifecycle_config_name: Option<String>,
    pub direct_internet_access: Option<DirectInternetAccess>,
    pub volume_size_in_gb: Option<i32>,
    pub accelerator_types: Option<Vec<NotebookInstanceAcceleratorType>>,
    pub default_code_repository: Option<String>,
    pub additional_code_repositories: Option<Vec<String>>,
    pub root_access: Option<RootAccess>,
    pub platform_identifier: Option<String>,
}

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.

notebook_instance_name: Option<String>

The name of the new notebook instance.

instance_type: Option<InstanceType>

The type of ML compute instance to launch for the notebook instance.

subnet_id: Option<String>

The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.

security_group_ids: Option<Vec<String>>

The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.

role_arn: Option<String>

When you send any requests to Amazon Web Services resources from the notebook instance, Amazon SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see Amazon SageMaker Roles.

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

kms_key_id: Option<String>

The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the Amazon Web Services Key Management Service Developer Guide.

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.

lifecycle_config_name: Option<String>

The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

direct_internet_access: Option<DirectInternetAccess>

Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this to Disabled this notebook instance is able to access resources only in your VPC, and is not be able to connect to Amazon SageMaker training and endpoint services unless you configure a NAT Gateway in your VPC.

For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.

volume_size_in_gb: Option<i32>

The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.

accelerator_types: Option<Vec<NotebookInstanceAcceleratorType>>

A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.

default_code_repository: Option<String>

A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

additional_code_repositories: Option<Vec<String>>

An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

root_access: Option<RootAccess>

Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.

Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.

platform_identifier: Option<String>

The platform identifier of the notebook instance runtime environment.

Implementations

impl CreateNotebookInstanceInput

pub async fn make_operation(
    &self,
    _config: &Config
) -> Result<Operation<CreateNotebookInstance, AwsErrorRetryPolicy>, BuildError>

Consumes the builder and constructs an Operation<CreateNotebookInstance>

pub fn builder() -> Builder

Creates a new builder-style object to manufacture CreateNotebookInstanceInput

impl CreateNotebookInstanceInput

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

The name of the new notebook instance.

pub fn instance_type(&self) -> Option<&InstanceType>

The type of ML compute instance to launch for the notebook instance.

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

The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.

pub fn security_group_ids(&self) -> Option<&[String]>

The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.

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

When you send any requests to Amazon Web Services resources from the notebook instance, Amazon SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see Amazon SageMaker Roles.

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

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

The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the Amazon Web Services Key Management Service Developer Guide.

pub fn tags(&self) -> Option<&[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.

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

The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

pub fn direct_internet_access(&self) -> Option<&DirectInternetAccess>

Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this to Disabled this notebook instance is able to access resources only in your VPC, and is not be able to connect to Amazon SageMaker training and endpoint services unless you configure a NAT Gateway in your VPC.

For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.

pub fn volume_size_in_gb(&self) -> Option<i32>

The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.

pub fn accelerator_types(&self) -> Option<&[NotebookInstanceAcceleratorType]>

A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.

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

A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

pub fn additional_code_repositories(&self) -> Option<&[String]>

An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

pub fn root_access(&self) -> Option<&RootAccess>

Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.

Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.

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

The platform identifier of the notebook instance runtime environment.

Trait Implementations

impl Clone for CreateNotebookInstanceInput

fn clone(&self) -> CreateNotebookInstanceInput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateNotebookInstanceInput

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

Formats the value using the given formatter.

impl PartialEq<CreateNotebookInstanceInput> for CreateNotebookInstanceInput

fn eq(&self, other: &CreateNotebookInstanceInput) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &CreateNotebookInstanceInput) -> bool

This method tests for !=.

impl StructuralPartialEq for CreateNotebookInstanceInput