Struct aws_sdk_sagemaker::model::training_job::Builder

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

A builder for TrainingJob

Implementations

impl Builder

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

The name of the training job.

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

The name of the training job.

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

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

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

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

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

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

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

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, input: impl Into<String>) -> Self

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

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

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

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

The Amazon Resource Name (ARN) of the job.

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

The Amazon Resource Name (ARN) of the job.

pub fn model_artifacts(self, input: ModelArtifacts) -> Self

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

pub fn set_model_artifacts(self, input: Option<ModelArtifacts>) -> Self

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

pub fn training_job_status(self, input: TrainingJobStatus) -> Self

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 set_training_job_status(self, input: Option<TrainingJobStatus>) -> Self

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, input: SecondaryStatus) -> Self

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.

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.

• 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 set_secondary_status(self, input: Option<SecondaryStatus>) -> Self

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.

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.

• 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, input: impl Into<String>) -> Self

If the training job failed, the reason it failed.

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

If the training job failed, the reason it failed.

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

Adds a key-value pair to hyper_parameters.

To override the contents of this collection use set_hyper_parameters.

Algorithm-specific parameters.

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

Algorithm-specific parameters.

pub fn algorithm_specification(self, input: AlgorithmSpecification) -> Self

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

pub fn set_algorithm_specification(
    self,
    input: Option<AlgorithmSpecification>
) -> Self

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

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

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

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

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

pub fn input_data_config(self, input: Channel) -> Self

Appends an item to input_data_config.

To override the contents of this collection use set_input_data_config.

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

pub fn set_input_data_config(self, input: Option<Vec<Channel>>) -> Self

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

pub fn output_data_config(self, input: OutputDataConfig) -> Self

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

pub fn set_output_data_config(self, input: Option<OutputDataConfig>) -> Self

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, input: ResourceConfig) -> Self

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

pub fn set_resource_config(self, input: Option<ResourceConfig>) -> Self

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

pub fn vpc_config(self, input: VpcConfig) -> Self

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 set_vpc_config(self, input: Option<VpcConfig>) -> Self

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, input: StoppingCondition) -> Self

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

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, input: DateTime) -> Self

A timestamp that indicates when the training job was created.

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

A timestamp that indicates when the training job was created.

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

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 set_training_start_time(self, input: Option<DateTime>) -> Self

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, input: DateTime) -> Self

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 set_training_end_time(self, input: Option<DateTime>) -> Self

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, input: DateTime) -> Self

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

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

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

pub fn secondary_status_transitions(
    self,
    input: SecondaryStatusTransition
) -> Self

Appends an item to secondary_status_transitions.

To override the contents of this collection use set_secondary_status_transitions.

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

pub fn set_secondary_status_transitions(
    self,
    input: Option<Vec<SecondaryStatusTransition>>
) -> Self

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

pub fn final_metric_data_list(self, input: MetricData) -> Self

Appends an item to final_metric_data_list.

To override the contents of this collection use set_final_metric_data_list.

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 set_final_metric_data_list(self, input: Option<Vec<MetricData>>) -> Self

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, input: bool) -> Self

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 set_enable_network_isolation(self, input: Option<bool>) -> Self

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, input: bool) -> Self

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 set_enable_inter_container_traffic_encryption(
    self,
    input: Option<bool>
) -> Self

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, input: bool) -> Self

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 set_enable_managed_spot_training(self, input: Option<bool>) -> Self

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, input: CheckpointConfig) -> Self

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

pub fn set_checkpoint_config(self, input: Option<CheckpointConfig>) -> Self

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

pub fn training_time_in_seconds(self, input: i32) -> Self

The training time in seconds.

pub fn set_training_time_in_seconds(self, input: Option<i32>) -> Self

The training time in seconds.

pub fn billable_time_in_seconds(self, input: i32) -> Self

The billable time in seconds.

pub fn set_billable_time_in_seconds(self, input: Option<i32>) -> Self

The billable time in seconds.

pub fn debug_hook_config(self, input: DebugHookConfig) -> Self

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 set_debug_hook_config(self, input: Option<DebugHookConfig>) -> Self

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, input: ExperimentConfig) -> Self

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 set_experiment_config(self, input: Option<ExperimentConfig>) -> Self

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, input: DebugRuleConfiguration) -> Self

Appends an item to debug_rule_configurations.

To override the contents of this collection use set_debug_rule_configurations.

Information about the debug rule configuration.

pub fn set_debug_rule_configurations(
    self,
    input: Option<Vec<DebugRuleConfiguration>>
) -> Self

Information about the debug rule configuration.

pub fn tensor_board_output_config(self, input: TensorBoardOutputConfig) -> Self

Configuration of storage locations for the Debugger TensorBoard output data.

pub fn set_tensor_board_output_config(
    self,
    input: Option<TensorBoardOutputConfig>
) -> Self

Configuration of storage locations for the Debugger TensorBoard output data.

pub fn debug_rule_evaluation_statuses(
    self,
    input: DebugRuleEvaluationStatus
) -> Self

Appends an item to debug_rule_evaluation_statuses.

To override the contents of this collection use set_debug_rule_evaluation_statuses.

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

pub fn set_debug_rule_evaluation_statuses(
    self,
    input: Option<Vec<DebugRuleEvaluationStatus>>
) -> Self

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

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

Adds a key-value pair to environment.

To override the contents of this collection use set_environment.

The environment variables to set in the Docker container.

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

The environment variables to set in the Docker container.

pub fn retry_strategy(self, input: RetryStrategy) -> Self

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

pub fn set_retry_strategy(self, input: Option<RetryStrategy>) -> Self

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

pub fn tags(self, input: Tag) -> Self

Appends an item to tags.

To override the contents of this collection use set_tags.

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.

pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

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.

pub fn build(self) -> TrainingJob

Consumes the builder and constructs a TrainingJob

Trait Implementations

impl Clone for Builder

fn clone(&self) -> Builder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Builder

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

Formats the value using the given formatter.

impl Default for Builder

fn default() -> Builder

Returns the “default value” for a type.

impl PartialEq<Builder> for Builder

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

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

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

This method tests for !=.

impl StructuralPartialEq for Builder