Struct aws_sdk_sagemaker::output::DescribeTrainingJobOutput

#[non_exhaustive]
pub struct DescribeTrainingJobOutput {

    pub training_job_name: Option<String>,
    pub training_job_arn: Option<String>,
    pub tuning_job_arn: Option<String>,
    pub labeling_job_arn: Option<String>,
    pub auto_ml_job_arn: Option<String>,
    pub model_artifacts: Option<ModelArtifacts>,
    pub training_job_status: Option<TrainingJobStatus>,
    pub secondary_status: Option<SecondaryStatus>,
    pub failure_reason: Option<String>,
    pub hyper_parameters: Option<HashMap<String, String>>,
    pub algorithm_specification: Option<AlgorithmSpecification>,
    pub role_arn: Option<String>,
    pub input_data_config: Option<Vec<Channel>>,
    pub output_data_config: Option<OutputDataConfig>,
    pub resource_config: Option<ResourceConfig>,
    pub vpc_config: Option<VpcConfig>,
    pub stopping_condition: Option<StoppingCondition>,
    pub creation_time: Option<DateTime>,
    pub training_start_time: Option<DateTime>,
    pub training_end_time: Option<DateTime>,
    pub last_modified_time: Option<DateTime>,
    pub secondary_status_transitions: Option<Vec<SecondaryStatusTransition>>,
    pub final_metric_data_list: Option<Vec<MetricData>>,
    pub enable_network_isolation: bool,
    pub enable_inter_container_traffic_encryption: bool,
    pub enable_managed_spot_training: bool,
    pub checkpoint_config: Option<CheckpointConfig>,
    pub training_time_in_seconds: Option<i32>,
    pub billable_time_in_seconds: Option<i32>,
    pub debug_hook_config: Option<DebugHookConfig>,
    pub experiment_config: Option<ExperimentConfig>,
    pub debug_rule_configurations: Option<Vec<DebugRuleConfiguration>>,
    pub tensor_board_output_config: Option<TensorBoardOutputConfig>,
    pub debug_rule_evaluation_statuses: Option<Vec<DebugRuleEvaluationStatus>>,
    pub profiler_config: Option<ProfilerConfig>,
    pub profiler_rule_configurations: Option<Vec<ProfilerRuleConfiguration>>,
    pub profiler_rule_evaluation_statuses: Option<Vec<ProfilerRuleEvaluationStatus>>,
    pub profiling_status: Option<ProfilingStatus>,
    pub retry_strategy: Option<RetryStrategy>,
    pub environment: Option<HashMap<String, String>>,
}

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.

training_job_name: Option<String>

Name of the model training job.

training_job_arn: Option<String>

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

tuning_job_arn: Option<String>

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

labeling_job_arn: Option<String>

The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

auto_ml_job_arn: Option<String>

The Amazon Resource Name (ARN) of an AutoML job.

model_artifacts: Option<ModelArtifacts>

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

training_job_status: Option<TrainingJobStatus>

The status of the training job.

Amazon SageMaker provides the following training job statuses:

• 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.

secondary_status: Option<SecondaryStatus>

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

Amazon 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.

• Interrupted - The job stopped because the managed spot training instances were interrupted.

• 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.

• MaxWaitTimeExceeded - The job stopped because it exceeded the maximum allowed wait time.

• 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

• PreparingTraining

• DownloadingTrainingImage

failure_reason: Option<String>

If the training job failed, the reason it failed.

hyper_parameters: Option<HashMap<String, String>>

Algorithm-specific parameters.

algorithm_specification: Option<AlgorithmSpecification>

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

role_arn: Option<String>

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

input_data_config: Option<Vec<Channel>>

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

output_data_config: Option<OutputDataConfig>

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

resource_config: Option<ResourceConfig>

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

vpc_config: 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.

stopping_condition: 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, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon 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.

creation_time: Option<DateTime>

A timestamp that indicates when the training job was created.

training_start_time: 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.

training_end_time: 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 Amazon SageMaker detects a job failure.

last_modified_time: Option<DateTime>

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

secondary_status_transitions: Option<Vec<SecondaryStatusTransition>>

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

final_metric_data_list: Option<Vec<MetricData>>

A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.

enable_network_isolation: bool

If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

enable_inter_container_traffic_encryption: 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 algorithms in distributed training.

enable_managed_spot_training: bool

A Boolean indicating whether managed spot training is enabled (True) or not (False).

checkpoint_config: Option<CheckpointConfig>

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

training_time_in_seconds: Option<i32>

The training time in seconds.

billable_time_in_seconds: Option<i32>

The billable time in seconds. Billable time refers to the absolute wall-clock time.

Multiply BillableTimeInSeconds by the number of instances (InstanceCount) in your training cluster to get the total compute time SageMaker will bill you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount .

You can calculate the savings from using managed spot training using the formula (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example, if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.

debug_hook_config: 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.

experiment_config: 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

debug_rule_configurations: Option<Vec<DebugRuleConfiguration>>

Configuration information for Debugger rules for debugging output tensors.

tensor_board_output_config: Option<TensorBoardOutputConfig>

Configuration of storage locations for the Debugger TensorBoard output data.

debug_rule_evaluation_statuses: Option<Vec<DebugRuleEvaluationStatus>>

Evaluation status of Debugger rules for debugging on a training job.

profiler_config: Option<ProfilerConfig>

Configuration information for Debugger system monitoring, framework profiling, and storage paths.

profiler_rule_configurations: Option<Vec<ProfilerRuleConfiguration>>

Configuration information for Debugger rules for profiling system and framework metrics.

profiler_rule_evaluation_statuses: Option<Vec<ProfilerRuleEvaluationStatus>>

Evaluation status of Debugger rules for profiling on a training job.

profiling_status: Option<ProfilingStatus>

Profiling status of a training job.

retry_strategy: Option<RetryStrategy>

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

environment: Option<HashMap<String, String>>

The environment variables to set in the Docker container.

Implementations

impl DescribeTrainingJobOutput

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

Name of the model 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 Amazon SageMaker Ground Truth labeling job that created the transform or training job.

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

The Amazon Resource Name (ARN) of an AutoML 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.

Amazon SageMaker provides the following training job statuses:

• 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 on the secondary status of the training job, see StatusMessage under SecondaryStatusTransition.

Amazon 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.

• Interrupted - The job stopped because the managed spot training instances were interrupted.

• 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.

• MaxWaitTimeExceeded - The job stopped because it exceeded the maximum allowed wait time.

• 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

• PreparingTraining

• 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. Amazon 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, Amazon SageMaker ends the training job. Use this API to cap model training costs.

To stop a job, Amazon 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 Amazon 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 collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.

pub fn enable_network_isolation(&self) -> bool

If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

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 algorithms in distributed training.

pub fn enable_managed_spot_training(&self) -> bool

A Boolean indicating whether managed spot training is enabled (True) or not (False).

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. Billable time refers to the absolute wall-clock time.

Multiply BillableTimeInSeconds by the number of instances (InstanceCount) in your training cluster to get the total compute time SageMaker will bill you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount .

You can calculate the savings from using managed spot training using the formula (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example, if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.

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]>

Configuration information for Debugger rules for debugging output tensors.

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]>

Evaluation status of Debugger rules for debugging on a training job.

pub fn profiler_config(&self) -> Option<&ProfilerConfig>

Configuration information for Debugger system monitoring, framework profiling, and storage paths.

pub fn profiler_rule_configurations(
    &self
) -> Option<&[ProfilerRuleConfiguration]>

Configuration information for Debugger rules for profiling system and framework metrics.

pub fn profiler_rule_evaluation_statuses(
    &self
) -> Option<&[ProfilerRuleEvaluationStatus]>

Evaluation status of Debugger rules for profiling on a training job.

pub fn profiling_status(&self) -> Option<&ProfilingStatus>

Profiling status of a training job.

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

The environment variables to set in the Docker container.

impl DescribeTrainingJobOutput

pub fn builder() -> Builder

Creates a new builder-style object to manufacture DescribeTrainingJobOutput

Trait Implementations

impl Clone for DescribeTrainingJobOutput

fn clone(&self) -> DescribeTrainingJobOutput

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for DescribeTrainingJobOutput

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

Formats the value using the given formatter.

impl PartialEq<DescribeTrainingJobOutput> for DescribeTrainingJobOutput

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

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

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

This method tests for !=.

impl StructuralPartialEq for DescribeTrainingJobOutput