aws_sdk_sagemaker::types

Struct TrainingJob

Source 
#[non_exhaustive]
pub struct TrainingJob {
Show 38 fields
    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: Option<bool>,
    pub enable_inter_container_traffic_encryption: Option<bool>,
    pub enable_managed_spot_training: Option<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 environment: Option<HashMap<String, String>>,
    pub retry_strategy: Option<RetryStrategy>,
    pub tags: Option<Vec<Tag>>,

}
Expand description

Contains information about a training job.

Fields (Non-exhaustive)§

This struct is marked as 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>

The name of the 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 labeling job.

§auto_ml_job_arn: Option<String>

The Amazon Resource Name (ARN) of the 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.

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.

§secondary_status: 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

§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.

Your input must be in the same Amazon Web Services region as your training job.

§output_data_config: Option<OutputDataConfig>

The S3 path where model artifacts that you configured when creating the job are stored. 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, 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.

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

§enable_network_isolation: Option<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.

§enable_inter_container_traffic_encryption: Option<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.

§enable_managed_spot_training: Option<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.

§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.

§debug_hook_config: Option<DebugHookConfig>

Configuration information for the Amazon SageMaker 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:

§debug_rule_configurations: Option<Vec<DebugRuleConfiguration>>

Information about the debug rule configuration.

§tensor_board_output_config: Option<TensorBoardOutputConfig>

Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.

§debug_rule_evaluation_statuses: Option<Vec<DebugRuleEvaluationStatus>>

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

§profiler_config: Option<ProfilerConfig>

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

§environment: Option<HashMap<String, String>>

The environment variables to set in the Docker container.

§retry_strategy: Option<RetryStrategy>

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

§tags: Option<Vec<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.

Implementations§

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impl TrainingJob

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

The name of the training job.

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

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

Source

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.

Source

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

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

Source

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

The Amazon Resource Name (ARN) of the job.

Source

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

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

Source

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.

Source

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

Source

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

If the training job failed, the reason it failed.

Source

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

Algorithm-specific parameters.

Source

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

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

Source

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

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

Source

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

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

Your input must be in the same Amazon Web Services region as your training job.

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

Source

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.

Source

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

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

Source

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.

Source

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.

Source

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

A timestamp that indicates when the training job was created.

Source

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.

Source

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.

Source

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

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

Source

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

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

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

Source

pub fn final_metric_data_list(&self) -> &[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.

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

Source

pub fn enable_network_isolation(&self) -> Option<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.

Source

pub fn enable_inter_container_traffic_encryption(&self) -> Option<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.

Source

pub fn enable_managed_spot_training(&self) -> Option<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.

Source

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

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

Source

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

The training time in seconds.

Source

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

The billable time in seconds.

Source

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

Configuration information for the Amazon SageMaker 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.

Source

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:

Source

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

Information about the debug rule configuration.

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

Source

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

Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.

Source

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

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

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

Source

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

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

Source

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

The environment variables to set in the Docker container.

Source

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

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

Source

pub fn tags(&self) -> &[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.

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().

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impl TrainingJob

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pub fn builder() -> TrainingJobBuilder

Creates a new builder-style object to manufacture TrainingJob.

Trait Implementations§

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impl Clone for TrainingJob

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fn clone(&self) -> TrainingJob

Returns a duplicate of the value. Read more
1.0.0 · Source§

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

Performs copy-assignment from source. Read more
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impl Debug for TrainingJob

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

Formats the value using the given formatter. Read more
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impl PartialEq for TrainingJob

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

Tests for self and other values to be equal, and is used by ==.
1.0.0 · Source§

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

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.
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impl StructuralPartialEq for TrainingJob

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Returns self with the bg() set to [Color :: BrightRed].

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fn on_bright_green(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightGreen].

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fn on_bright_yellow(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightYellow].

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fn on_bright_blue(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightBlue].

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fn on_bright_magenta(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightMagenta].

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fn on_bright_cyan(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightCyan].

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fn on_bright_white(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightWhite].

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fn attr(&self, value: Attribute) -> Painted<&T>

Enables the styling Attribute value.

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Make text bold using attr():

use yansi::{Paint, Attribute};

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Make text bold using using bold().

use yansi::Paint;

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fn bold(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Bold].

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Returns self with the attr() set to [Attribute :: Dim].

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fn italic(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Italic].

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Returns self with the attr() set to [Attribute :: Underline].

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Returns self with the attr() set to [Attribute :: Blink].

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Returns self with the attr() set to [Attribute :: RapidBlink].

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fn invert(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Invert].

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fn conceal(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Conceal].

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fn strike(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Strike].

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fn quirk(&self, value: Quirk) -> Painted<&T>

Enables the yansi Quirk value.

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Enable wrapping using wrap().

use yansi::Paint;

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fn mask(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Mask].

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Returns self with the quirk() set to [Quirk :: Wrap].

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fn linger(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Linger].

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fn clear(&self) -> Painted<&T>

👎Deprecated since 1.0.1: renamed to resetting() due to conflicts with Vec::clear(). The clear() method will be removed in a future release.

Returns self with the quirk() set to [Quirk :: Clear].

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fn resetting(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Resetting].

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fn bright(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Bright].

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fn on_bright(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: OnBright].

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fn whenever(&self, value: Condition) -> Painted<&T>

Conditionally enable styling based on whether the Condition value applies. Replaces any previous condition.

See the crate level docs for more details.

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Enable styling painted only when both stdout and stderr are TTYs:

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fn new(self) -> Painted<Self>
where Self: Sized,

Create a new Painted with a default Style. Read more
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fn paint<S>(&self, style: S) -> Painted<&Self>
where S: Into<Style>,

Apply a style wholesale to self. Any previous style is replaced. Read more
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impl<T> Same for T

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type Output = T

Should always be Self
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type Owned = T

The resulting type after obtaining ownership.
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fn to_owned(&self) -> T

Creates owned data from borrowed data, usually by cloning. Read more
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fn clone_into(&self, target: &mut T)

Uses borrowed data to replace owned data, usually by cloning. Read more
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impl<T, U> TryFrom<U> for T
where U: Into<T>,

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type Error = Infallible

The type returned in the event of a conversion error.
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fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
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impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

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type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
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fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.
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where S: Into<Dispatch>,

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impl<T> ErasedDestructor for T
where T: 'static,