Struct StartModelInput 

#[non_exhaustive]
pub struct StartModelInput {
    pub project_name: Option<String>,
    pub model_version: Option<String>,
    pub min_inference_units: Option<i32>,
    pub client_token: Option<String>,
    pub max_inference_units: Option<i32>,
}

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.

project_name: Option<String>

The name of the project that contains the model that you want to start.

model_version: Option<String>

The version of the model that you want to start.

min_inference_units: Option<i32>

The minimum number of inference units to use. A single inference unit represents 1 hour of processing. Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use.

client_token: Option<String>

ClientToken is an idempotency token that ensures a call to StartModel completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from StartModel. In this case, safely retry your call to StartModel by using the same ClientToken parameter value.

If you don't supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple start requests. You'll need to provide your own value for other use cases.

An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to StartModel. An idempotency token is active for 8 hours.

max_inference_units: Option<i32>

The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Lookout for Vision doesn't auto-scale the model.

Implementations

impl StartModelInput

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

The name of the project that contains the model that you want to start.

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

The version of the model that you want to start.

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

The minimum number of inference units to use. A single inference unit represents 1 hour of processing. Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use.

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

ClientToken is an idempotency token that ensures a call to StartModel completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from StartModel. In this case, safely retry your call to StartModel by using the same ClientToken parameter value.

If you don't supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple start requests. You'll need to provide your own value for other use cases.

An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to StartModel. An idempotency token is active for 8 hours.

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

The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Lookout for Vision doesn't auto-scale the model.

impl StartModelInput

pub fn builder() -> StartModelInputBuilder

Creates a new builder-style object to manufacture StartModelInput.

Trait Implementations

impl Clone for StartModelInput

fn clone(&self) -> StartModelInput

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for StartModelInput

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

Formats the value using the given formatter.

impl PartialEq for StartModelInput

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