Struct aws_sdk_sagemaker::model::TrainingJob

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

Contains information about a training job.

Implementations

impl TrainingJob

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

The name of the training job.

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

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

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

The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.

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

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

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

The Amazon Resource Name (ARN) of the job.

pub fn model_artifacts(&self) -> Option<&ModelArtifacts>

Information about the Amazon S3 location that is configured for storing model artifacts.

pub fn training_job_status(&self) -> Option<&TrainingJobStatus>

The status of the training job.

Training job statuses are:

• InProgress - The training is in progress.

• Completed - The training job has completed.

• Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.

• Stopping - The training job is stopping.

• Stopped - The training job has stopped.

For more detailed information, see SecondaryStatus.

pub fn secondary_status(&self) -> Option<&SecondaryStatus>

Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition.

SageMaker provides primary statuses and secondary statuses that apply to each of them:

InProgress

• Starting - Starting the training job.

• Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.

• Training - Training is in progress.

• Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.

Completed

• Completed - The training job has completed.

Failed

• Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.

Stopped

• MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.

• Stopped - The training job has stopped.

Stopping

• Stopping - Stopping the training job.

Valid values for SecondaryStatus are subject to change.

We no longer support the following secondary statuses:

• LaunchingMLInstances

• PreparingTrainingStack

• DownloadingTrainingImage

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

If the training job failed, the reason it failed.

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

Algorithm-specific parameters.

pub fn algorithm_specification(&self) -> Option<&AlgorithmSpecification>

Information about the algorithm used for training, and algorithm metadata.

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

The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.

pub fn input_data_config(&self) -> Option<&[Channel]>

An array of Channel objects that describes each data input channel.

pub fn output_data_config(&self) -> Option<&OutputDataConfig>

The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.

pub fn resource_config(&self) -> Option<&ResourceConfig>

Resources, including ML compute instances and ML storage volumes, that are configured for model training.

pub fn vpc_config(&self) -> Option<&VpcConfig>

A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.

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

Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

To stop a 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.

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

A timestamp that indicates when the training job was created.

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

Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.

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

Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.

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

A timestamp that indicates when the status of the training job was last modified.

pub fn secondary_status_transitions(
    &self
) -> Option<&[SecondaryStatusTransition]>

A history of all of the secondary statuses that the training job has transitioned through.

pub fn final_metric_data_list(&self) -> Option<&[MetricData]>

A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.

pub fn enable_network_isolation(&self) -> bool

If the TrainingJob was created with network isolation, the value is set to true. If network isolation is enabled, nodes can't communicate beyond the VPC they run in.

pub fn enable_inter_container_traffic_encryption(&self) -> bool

To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.

pub fn enable_managed_spot_training(&self) -> bool

When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training.

pub fn checkpoint_config(&self) -> Option<&CheckpointConfig>

Contains information about the output location for managed spot training checkpoint data.

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

The training time in seconds.

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

The billable time in seconds.

pub fn debug_hook_config(&self) -> Option<&DebugHookConfig>

Configuration information for the Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.

pub fn experiment_config(&self) -> Option<&ExperimentConfig>

Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs:

• CreateProcessingJob

• CreateTrainingJob

• CreateTransformJob

pub fn debug_rule_configurations(&self) -> Option<&[DebugRuleConfiguration]>

Information about the debug rule configuration.

pub fn tensor_board_output_config(&self) -> Option<&TensorBoardOutputConfig>

Configuration of storage locations for the Debugger TensorBoard output data.

pub fn debug_rule_evaluation_statuses(
    &self
) -> Option<&[DebugRuleEvaluationStatus]>

Information about the evaluation status of the rules for the training job.

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

The environment variables to set in the Docker container.

pub fn retry_strategy(&self) -> Option<&RetryStrategy>

The number of times to retry the job when the job fails due to an InternalServerError.

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

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

impl TrainingJob

pub fn builder() -> Builder

Creates a new builder-style object to manufacture TrainingJob.

Trait Implementations

impl Clone for TrainingJob

fn clone(&self) -> TrainingJob

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for TrainingJob

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

Formats the value using the given formatter.

impl PartialEq<TrainingJob> for TrainingJob

fn eq(&self, other: &TrainingJob) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for TrainingJob