Struct aws_sdk_sagemaker::operation::describe_optimization_job::builders::DescribeOptimizationJobOutputBuilder

#[non_exhaustive]
pub struct DescribeOptimizationJobOutputBuilder { /* private fields */ }

A builder for DescribeOptimizationJobOutput.

Implementations

impl DescribeOptimizationJobOutputBuilder

pub fn optimization_job_arn(self, input: impl Into<String>) -> Self

The Amazon Resource Name (ARN) of the optimization job.

This field is required.

pub fn set_optimization_job_arn(self, input: Option<String>) -> Self

The Amazon Resource Name (ARN) of the optimization job.

pub fn get_optimization_job_arn(&self) -> &Option<String>

The Amazon Resource Name (ARN) of the optimization job.

pub fn optimization_job_status(self, input: OptimizationJobStatus) -> Self

The current status of the optimization job.

This field is required.

pub fn set_optimization_job_status( self, input: Option<OptimizationJobStatus>, ) -> Self

The current status of the optimization job.

pub fn get_optimization_job_status(&self) -> &Option<OptimizationJobStatus>

The current status of the optimization job.

pub fn optimization_start_time(self, input: DateTime) -> Self

The time when the optimization job started.

pub fn set_optimization_start_time(self, input: Option<DateTime>) -> Self

The time when the optimization job started.

pub fn get_optimization_start_time(&self) -> &Option<DateTime>

The time when the optimization job started.

pub fn optimization_end_time(self, input: DateTime) -> Self

The time when the optimization job finished processing.

pub fn set_optimization_end_time(self, input: Option<DateTime>) -> Self

The time when the optimization job finished processing.

pub fn get_optimization_end_time(&self) -> &Option<DateTime>

The time when the optimization job finished processing.

pub fn creation_time(self, input: DateTime) -> Self

The time when you created the optimization job.

This field is required.

pub fn set_creation_time(self, input: Option<DateTime>) -> Self

The time when you created the optimization job.

pub fn get_creation_time(&self) -> &Option<DateTime>

The time when you created the optimization job.

pub fn last_modified_time(self, input: DateTime) -> Self

The time when the optimization job was last updated.

This field is required.

pub fn set_last_modified_time(self, input: Option<DateTime>) -> Self

The time when the optimization job was last updated.

pub fn get_last_modified_time(&self) -> &Option<DateTime>

The time when the optimization job was last updated.

pub fn failure_reason(self, input: impl Into<String>) -> Self

If the optimization job status is FAILED, the reason for the failure.

pub fn set_failure_reason(self, input: Option<String>) -> Self

If the optimization job status is FAILED, the reason for the failure.

pub fn get_failure_reason(&self) -> &Option<String>

If the optimization job status is FAILED, the reason for the failure.

pub fn optimization_job_name(self, input: impl Into<String>) -> Self

The name that you assigned to the optimization job.

This field is required.

pub fn set_optimization_job_name(self, input: Option<String>) -> Self

The name that you assigned to the optimization job.

pub fn get_optimization_job_name(&self) -> &Option<String>

The name that you assigned to the optimization job.

pub fn model_source(self, input: OptimizationJobModelSource) -> Self

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

This field is required.

pub fn set_model_source(self, input: Option<OptimizationJobModelSource>) -> Self

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

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

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

pub fn optimization_environment( self, k: impl Into<String>, v: impl Into<String>, ) -> Self

Adds a key-value pair to optimization_environment.

To override the contents of this collection use set_optimization_environment.

The environment variables to set in the model container.

pub fn set_optimization_environment( self, input: Option<HashMap<String, String>>, ) -> Self

The environment variables to set in the model container.

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

The environment variables to set in the model container.

pub fn deployment_instance_type( self, input: OptimizationJobDeploymentInstanceType, ) -> Self

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

This field is required.

pub fn set_deployment_instance_type( self, input: Option<OptimizationJobDeploymentInstanceType>, ) -> Self

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

pub fn get_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_configs(self, input: OptimizationConfig) -> Self

Appends an item to optimization_configs.

To override the contents of this collection use set_optimization_configs.

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

pub fn set_optimization_configs( self, input: Option<Vec<OptimizationConfig>>, ) -> Self

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

pub fn get_optimization_configs(&self) -> &Option<Vec<OptimizationConfig>>

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

pub fn output_config(self, input: OptimizationJobOutputConfig) -> Self

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

This field is required.

pub fn set_output_config( self, input: Option<OptimizationJobOutputConfig>, ) -> Self

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

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

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

pub fn optimization_output(self, input: OptimizationOutput) -> Self

Output values produced by an optimization job.

pub fn set_optimization_output(self, input: Option<OptimizationOutput>) -> Self

Output values produced by an optimization job.

pub fn get_optimization_output(&self) -> &Option<OptimizationOutput>

Output values produced by an optimization job.

pub fn role_arn(self, input: impl Into<String>) -> Self

The ARN of the IAM role that you assigned to the optimization job.

This field is required.

pub fn set_role_arn(self, input: Option<String>) -> Self

The ARN of the IAM role that you assigned to the optimization job.

pub fn get_role_arn(&self) -> &Option<String>

The ARN of the IAM role that you assigned to the optimization job.

pub fn stopping_condition(self, input: StoppingCondition) -> Self

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.

This field is required.

pub fn set_stopping_condition(self, input: Option<StoppingCondition>) -> Self

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 get_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 vpc_config(self, input: OptimizationVpcConfig) -> Self

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

pub fn set_vpc_config(self, input: Option<OptimizationVpcConfig>) -> Self

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

pub fn get_vpc_config(&self) -> &Option<OptimizationVpcConfig>

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

pub fn build(self) -> DescribeOptimizationJobOutput

Consumes the builder and constructs a DescribeOptimizationJobOutput.

Trait Implementations

impl Clone for DescribeOptimizationJobOutputBuilder

fn clone(&self) -> DescribeOptimizationJobOutputBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for DescribeOptimizationJobOutputBuilder

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

Formats the value using the given formatter.

impl Default for DescribeOptimizationJobOutputBuilder

fn default() -> DescribeOptimizationJobOutputBuilder

Returns the “default value” for a type.

impl PartialEq for DescribeOptimizationJobOutputBuilder

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