Struct CreateOptimizationJobInput

#[non_exhaustive]
pub struct CreateOptimizationJobInput {
    pub optimization_job_name: Option<String>,
    pub role_arn: Option<String>,
    pub model_source: Option<OptimizationJobModelSource>,
    pub deployment_instance_type: Option<OptimizationJobDeploymentInstanceType>,
    pub optimization_environment: Option<HashMap<String, String>>,
    pub optimization_configs: Option<Vec<OptimizationConfig>>,
    pub output_config: Option<OptimizationJobOutputConfig>,
    pub stopping_condition: Option<StoppingCondition>,
    pub tags: Option<Vec<Tag>>,
    pub vpc_config: Option<OptimizationVpcConfig>,
}

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.

optimization_job_name: Option<String>

A custom name for the new optimization job.

role_arn: Option<String>

The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.

During model optimization, Amazon SageMaker AI needs your permission to:

• Read input data from an S3 bucket

• Write model artifacts to an S3 bucket

• Write logs to Amazon CloudWatch Logs

• Publish metrics to Amazon CloudWatch

You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.

model_source: Option<OptimizationJobModelSource>

The location of the source model to optimize with an optimization job.

deployment_instance_type: Option<OptimizationJobDeploymentInstanceType>

The type of instance that hosts the optimized model that you create with the optimization job.

optimization_environment: Option<HashMap<String, String>>

The environment variables to set in the model container.

optimization_configs: Option<Vec<OptimizationConfig>>

Settings for each of the optimization techniques that the job applies.

output_config: Option<OptimizationJobOutputConfig>

Details for where to store the optimized model that you create with the optimization job.

stopping_condition: Option<StoppingCondition>

Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.

To stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel.

The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.

tags: Option<Vec<Tag>>

A list of key-value pairs associated with the optimization job. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

vpc_config: Option<OptimizationVpcConfig>

A VPC in Amazon VPC that your optimized model has access to.

Implementations

impl CreateOptimizationJobInput

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

A custom name for the new optimization job.

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

The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.

During model optimization, Amazon SageMaker AI needs your permission to:

• Read input data from an S3 bucket

• Write model artifacts to an S3 bucket

• Write logs to Amazon CloudWatch Logs

• Publish metrics to Amazon CloudWatch

You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.

pub fn model_source(&self) -> Option<&OptimizationJobModelSource>

The location of the source model to optimize with an optimization job.

pub fn deployment_instance_type( &self, ) -> Option<&OptimizationJobDeploymentInstanceType>

The type of instance that hosts the optimized model that you create with the optimization job.

pub fn optimization_environment(&self) -> Option<&HashMap<String, String>>

The environment variables to set in the model container.

pub fn optimization_configs(&self) -> &[OptimizationConfig]

Settings for each of the optimization techniques that the job applies.

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

pub fn output_config(&self) -> Option<&OptimizationJobOutputConfig>

Details for where to store the optimized model that you create with the optimization job.

pub fn stopping_condition(&self) -> Option<&StoppingCondition>

Specifies a limit to how long a job can run. When the job reaches the time limit, SageMaker ends the job. Use this API to cap costs.

To stop a training job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

The training algorithms provided by SageMaker automatically save the intermediate results of a model training job when possible. This attempt to save artifacts is only a best effort case as model might not be in a state from which it can be saved. For example, if training has just started, the model might not be ready to save. When saved, this intermediate data is a valid model artifact. You can use it to create a model with CreateModel.

The Neural Topic Model (NTM) currently does not support saving intermediate model artifacts. When training NTMs, make sure that the maximum runtime is sufficient for the training job to complete.

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

A list of key-value pairs associated with the optimization job. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

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<&OptimizationVpcConfig>

A VPC in Amazon VPC that your optimized model has access to.

impl CreateOptimizationJobInput

pub fn builder() -> CreateOptimizationJobInputBuilder

Creates a new builder-style object to manufacture CreateOptimizationJobInput.

Trait Implementations

impl Clone for CreateOptimizationJobInput

fn clone(&self) -> CreateOptimizationJobInput

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CreateOptimizationJobInput

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

Formats the value using the given formatter.

impl PartialEq for CreateOptimizationJobInput

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