Struct aws_sdk_sagemaker::operation::create_mlflow_tracking_server::CreateMlflowTrackingServerInput

#[non_exhaustive]
pub struct CreateMlflowTrackingServerInput {
    pub tracking_server_name: Option<String>,
    pub artifact_store_uri: Option<String>,
    pub tracking_server_size: Option<TrackingServerSize>,
    pub mlflow_version: Option<String>,
    pub role_arn: Option<String>,
    pub automatic_model_registration: Option<bool>,
    pub weekly_maintenance_window_start: Option<String>,
    pub tags: Option<Vec<Tag>>,
}

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.

tracking_server_name: Option<String>

A unique string identifying the tracking server name. This string is part of the tracking server ARN.

artifact_store_uri: Option<String>

The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.

tracking_server_size: Option<TrackingServerSize>

The size of the tracking server you want to create. You can choose between "Small", "Medium", and "Large". The default MLflow Tracking Server configuration size is "Small". You can choose a size depending on the projected use of the tracking server such as the volume of data logged, number of users, and frequency of use.

We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.

mlflow_version: Option<String>

The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see How it works.

role_arn: Option<String>

The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the artifact store in Amazon S3. The role should have AmazonS3FullAccess permissions. For more information on IAM permissions for tracking server creation, see Set up IAM permissions for MLflow.

automatic_model_registration: Option<bool>

Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to True. To disable automatic model registration, set this value to False. If not specified, AutomaticModelRegistration defaults to False.

weekly_maintenance_window_start: Option<String>

The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.

tags: Option<Vec<Tag>>

Tags consisting of key-value pairs used to manage metadata for the tracking server.

Implementations

impl CreateMlflowTrackingServerInput

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

A unique string identifying the tracking server name. This string is part of the tracking server ARN.

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

The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.

pub fn tracking_server_size(&self) -> Option<&TrackingServerSize>

The size of the tracking server you want to create. You can choose between "Small", "Medium", and "Large". The default MLflow Tracking Server configuration size is "Small". You can choose a size depending on the projected use of the tracking server such as the volume of data logged, number of users, and frequency of use.

We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users.

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

The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see How it works.

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

The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the artifact store in Amazon S3. The role should have AmazonS3FullAccess permissions. For more information on IAM permissions for tracking server creation, see Set up IAM permissions for MLflow.

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

Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to True. To disable automatic model registration, set this value to False. If not specified, AutomaticModelRegistration defaults to False.

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

The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.

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

Tags consisting of key-value pairs used to manage metadata for the tracking server.

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().

impl CreateMlflowTrackingServerInput

pub fn builder() -> CreateMlflowTrackingServerInputBuilder

Creates a new builder-style object to manufacture CreateMlflowTrackingServerInput.

Trait Implementations

impl Clone for CreateMlflowTrackingServerInput

fn clone(&self) -> CreateMlflowTrackingServerInput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateMlflowTrackingServerInput

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

Formats the value using the given formatter.

impl PartialEq for CreateMlflowTrackingServerInput

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