#[non_exhaustive]pub struct DescribeOptimizationJobOutput {Show 17 fields
pub optimization_job_arn: Option<String>,
pub optimization_job_status: Option<OptimizationJobStatus>,
pub optimization_start_time: Option<DateTime>,
pub optimization_end_time: Option<DateTime>,
pub creation_time: Option<DateTime>,
pub last_modified_time: Option<DateTime>,
pub failure_reason: Option<String>,
pub optimization_job_name: Option<String>,
pub model_source: Option<OptimizationJobModelSource>,
pub optimization_environment: Option<HashMap<String, String>>,
pub deployment_instance_type: Option<OptimizationJobDeploymentInstanceType>,
pub optimization_configs: Option<Vec<OptimizationConfig>>,
pub output_config: Option<OptimizationJobOutputConfig>,
pub optimization_output: Option<OptimizationOutput>,
pub role_arn: Option<String>,
pub stopping_condition: Option<StoppingCondition>,
pub vpc_config: Option<OptimizationVpcConfig>,
/* private fields */
}Fields (Non-exhaustive)§
This struct is marked as non-exhaustive
Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.optimization_job_arn: Option<String>The Amazon Resource Name (ARN) of the optimization job.
optimization_job_status: Option<OptimizationJobStatus>The current status of the optimization job.
optimization_start_time: Option<DateTime>The time when the optimization job started.
optimization_end_time: Option<DateTime>The time when the optimization job finished processing.
creation_time: Option<DateTime>The time when you created the optimization job.
last_modified_time: Option<DateTime>The time when the optimization job was last updated.
failure_reason: Option<String>If the optimization job status is FAILED, the reason for the failure.
optimization_job_name: Option<String>The name that you assigned to the optimization job.
model_source: Option<OptimizationJobModelSource>The location of the source model to optimize with an optimization job.
optimization_environment: Option<HashMap<String, String>>The environment variables to set in the model container.
deployment_instance_type: Option<OptimizationJobDeploymentInstanceType>The type of instance that hosts the optimized model that you create with the optimization job.
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.
optimization_output: Option<OptimizationOutput>Output values produced by an optimization job.
role_arn: Option<String>The ARN of the IAM role that you assigned to 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.
vpc_config: Option<OptimizationVpcConfig>A VPC in Amazon VPC that your optimized model has access to.
Implementations§
Source§impl DescribeOptimizationJobOutput
impl DescribeOptimizationJobOutput
Sourcepub fn optimization_job_arn(&self) -> Option<&str>
pub fn optimization_job_arn(&self) -> Option<&str>
The Amazon Resource Name (ARN) of the optimization job.
Sourcepub fn optimization_job_status(&self) -> Option<&OptimizationJobStatus>
pub fn optimization_job_status(&self) -> Option<&OptimizationJobStatus>
The current status of the optimization job.
Sourcepub fn optimization_start_time(&self) -> Option<&DateTime>
pub fn optimization_start_time(&self) -> Option<&DateTime>
The time when the optimization job started.
Sourcepub fn optimization_end_time(&self) -> Option<&DateTime>
pub fn optimization_end_time(&self) -> Option<&DateTime>
The time when the optimization job finished processing.
Sourcepub fn creation_time(&self) -> Option<&DateTime>
pub fn creation_time(&self) -> Option<&DateTime>
The time when you created the optimization job.
Sourcepub fn last_modified_time(&self) -> Option<&DateTime>
pub fn last_modified_time(&self) -> Option<&DateTime>
The time when the optimization job was last updated.
Sourcepub fn failure_reason(&self) -> Option<&str>
pub fn failure_reason(&self) -> Option<&str>
If the optimization job status is FAILED, the reason for the failure.
Sourcepub fn optimization_job_name(&self) -> Option<&str>
pub fn optimization_job_name(&self) -> Option<&str>
The name that you assigned to the optimization job.
Sourcepub fn model_source(&self) -> Option<&OptimizationJobModelSource>
pub fn model_source(&self) -> Option<&OptimizationJobModelSource>
The location of the source model to optimize with an optimization job.
Sourcepub fn optimization_environment(&self) -> Option<&HashMap<String, String>>
pub fn optimization_environment(&self) -> Option<&HashMap<String, String>>
The environment variables to set in the model container.
Sourcepub fn deployment_instance_type(
&self,
) -> Option<&OptimizationJobDeploymentInstanceType>
pub fn deployment_instance_type( &self, ) -> Option<&OptimizationJobDeploymentInstanceType>
The type of instance that hosts the optimized model that you create with the optimization job.
Sourcepub fn optimization_configs(&self) -> &[OptimizationConfig]
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().
Sourcepub fn output_config(&self) -> Option<&OptimizationJobOutputConfig>
pub fn output_config(&self) -> Option<&OptimizationJobOutputConfig>
Details for where to store the optimized model that you create with the optimization job.
Sourcepub fn optimization_output(&self) -> Option<&OptimizationOutput>
pub fn optimization_output(&self) -> Option<&OptimizationOutput>
Output values produced by an optimization job.
Sourcepub fn role_arn(&self) -> Option<&str>
pub fn role_arn(&self) -> Option<&str>
The ARN of the IAM role that you assigned to the optimization job.
Sourcepub fn stopping_condition(&self) -> Option<&StoppingCondition>
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.
Sourcepub fn vpc_config(&self) -> Option<&OptimizationVpcConfig>
pub fn vpc_config(&self) -> Option<&OptimizationVpcConfig>
A VPC in Amazon VPC that your optimized model has access to.
Source§impl DescribeOptimizationJobOutput
impl DescribeOptimizationJobOutput
Sourcepub fn builder() -> DescribeOptimizationJobOutputBuilder
pub fn builder() -> DescribeOptimizationJobOutputBuilder
Creates a new builder-style object to manufacture DescribeOptimizationJobOutput.
Trait Implementations§
Source§impl Clone for DescribeOptimizationJobOutput
impl Clone for DescribeOptimizationJobOutput
Source§fn clone(&self) -> DescribeOptimizationJobOutput
fn clone(&self) -> DescribeOptimizationJobOutput
1.0.0 · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source. Read moreSource§impl PartialEq for DescribeOptimizationJobOutput
impl PartialEq for DescribeOptimizationJobOutput
Source§fn eq(&self, other: &DescribeOptimizationJobOutput) -> bool
fn eq(&self, other: &DescribeOptimizationJobOutput) -> bool
self and other values to be equal, and is used by ==.Source§impl RequestId for DescribeOptimizationJobOutput
impl RequestId for DescribeOptimizationJobOutput
Source§fn request_id(&self) -> Option<&str>
fn request_id(&self) -> Option<&str>
None if the service could not be reached.impl StructuralPartialEq for DescribeOptimizationJobOutput
Auto Trait Implementations§
impl Freeze for DescribeOptimizationJobOutput
impl RefUnwindSafe for DescribeOptimizationJobOutput
impl Send for DescribeOptimizationJobOutput
impl Sync for DescribeOptimizationJobOutput
impl Unpin for DescribeOptimizationJobOutput
impl UnwindSafe for DescribeOptimizationJobOutput
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