aws_sdk_sagemaker::operation::describe_training_job::builders

Struct DescribeTrainingJobOutputBuilder

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#[non_exhaustive]
pub struct DescribeTrainingJobOutputBuilder { /* private fields */ }
Expand description

A builder for DescribeTrainingJobOutput.

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impl DescribeTrainingJobOutputBuilder

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

Name of the model training job.

This field is required.
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pub fn set_training_job_name(self, input: Option<String>) -> Self

Name of the model training job.

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pub fn get_training_job_name(&self) -> &Option<String>

Name of the model training job.

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

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

This field is required.
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pub fn set_training_job_arn(self, input: Option<String>) -> Self

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

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pub fn get_training_job_arn(&self) -> &Option<String>

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

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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.

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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.

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pub fn get_tuning_job_arn(&self) -> &Option<String>

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

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

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

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

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

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pub fn get_labeling_job_arn(&self) -> &Option<String>

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

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

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

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

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

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pub fn get_auto_ml_job_arn(&self) -> &Option<String>

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

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

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

This field is required.
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pub fn set_model_artifacts(self, input: Option<ModelArtifacts>) -> Self

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

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

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

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

The status of the training job.

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.

This field is required.
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pub fn set_training_job_status(self, input: Option<TrainingJobStatus>) -> Self

The status of the training job.

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.

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

The status of the training job.

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.

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pub fn secondary_status(self, input: SecondaryStatus) -> Self

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.

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

This field is required.
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pub fn set_secondary_status(self, input: Option<SecondaryStatus>) -> Self

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.

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

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pub fn get_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.

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

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

If the training job failed, the reason it failed.

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

If the training job failed, the reason it failed.

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pub fn get_failure_reason(&self) -> &Option<String>

If the training job failed, the reason it failed.

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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.

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

Algorithm-specific parameters.

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

Algorithm-specific parameters.

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

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

This field is required.
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pub fn set_algorithm_specification( self, input: Option<AlgorithmSpecification>, ) -> Self

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

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

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

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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.

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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.

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pub fn get_role_arn(&self) -> &Option<String>

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

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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.

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

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

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pub fn get_input_data_config(&self) -> &Option<Vec<Channel>>

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

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

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

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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. SageMaker creates subfolders for model artifacts.

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pub fn get_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.

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pub fn resource_config(self, input: ResourceConfig) -> Self

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

This field is required.
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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.

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

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

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pub fn warm_pool_status(self, input: WarmPoolStatus) -> Self

The status of the warm pool associated with the training job.

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pub fn set_warm_pool_status(self, input: Option<WarmPoolStatus>) -> Self

The status of the warm pool associated with the training job.

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pub fn get_warm_pool_status(&self) -> &Option<WarmPoolStatus>

The status of the warm pool associated with the training job.

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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.

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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.

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pub fn get_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.

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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, 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.

This field is required.
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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, 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.

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pub fn get_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.

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

A timestamp that indicates when the training job was created.

This field is required.
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pub fn set_creation_time(self, input: Option<DateTime>) -> Self

A timestamp that indicates when the training job was created.

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

A timestamp that indicates when the training job was created.

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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.

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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.

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pub fn get_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.

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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 SageMaker detects a job failure.

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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 SageMaker detects a job failure.

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pub fn get_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.

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

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

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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.

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

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

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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.

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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.

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pub fn get_secondary_status_transitions( &self, ) -> &Option<Vec<SecondaryStatusTransition>>

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

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

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

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

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pub fn get_final_metric_data_list(&self) -> &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.

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pub fn enable_network_isolation(self, input: bool) -> Self

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, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

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

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, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

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pub fn get_enable_network_isolation(&self) -> &Option<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, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

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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 algorithms in distributed training.

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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 algorithms in distributed training.

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pub fn get_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 algorithms in distributed training.

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pub fn enable_managed_spot_training(self, input: bool) -> Self

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

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

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

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pub fn get_enable_managed_spot_training(&self) -> &Option<bool>

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

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pub fn checkpoint_config(self, input: CheckpointConfig) -> Self

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

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

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

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

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

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

The training time in seconds.

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

The training time in seconds.

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

The training time in seconds.

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

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 bills 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%.

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

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 bills 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%.

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pub fn get_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 bills 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%.

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

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.

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

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.

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pub fn get_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.

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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:

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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:

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pub fn get_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:

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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.

Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.

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

Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.

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pub fn get_debug_rule_configurations( &self, ) -> &Option<Vec<DebugRuleConfiguration>>

Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.

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

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

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

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

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

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

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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.

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

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

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

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pub fn get_debug_rule_evaluation_statuses( &self, ) -> &Option<Vec<DebugRuleEvaluationStatus>>

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

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pub fn profiler_config(self, input: ProfilerConfig) -> Self

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

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pub fn set_profiler_config(self, input: Option<ProfilerConfig>) -> Self

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

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

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

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pub fn profiler_rule_configurations( self, input: ProfilerRuleConfiguration, ) -> Self

Appends an item to profiler_rule_configurations.

To override the contents of this collection use set_profiler_rule_configurations.

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

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pub fn set_profiler_rule_configurations( self, input: Option<Vec<ProfilerRuleConfiguration>>, ) -> Self

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

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pub fn get_profiler_rule_configurations( &self, ) -> &Option<Vec<ProfilerRuleConfiguration>>

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

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pub fn profiler_rule_evaluation_statuses( self, input: ProfilerRuleEvaluationStatus, ) -> Self

Appends an item to profiler_rule_evaluation_statuses.

To override the contents of this collection use set_profiler_rule_evaluation_statuses.

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

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pub fn set_profiler_rule_evaluation_statuses( self, input: Option<Vec<ProfilerRuleEvaluationStatus>>, ) -> Self

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

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pub fn get_profiler_rule_evaluation_statuses( &self, ) -> &Option<Vec<ProfilerRuleEvaluationStatus>>

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

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pub fn profiling_status(self, input: ProfilingStatus) -> Self

Profiling status of a training job.

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pub fn set_profiling_status(self, input: Option<ProfilingStatus>) -> Self

Profiling status of a training job.

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

Profiling status of a training job.

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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.

Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.

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

The environment variables to set in the Docker container.

Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.

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

The environment variables to set in the Docker container.

Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.

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

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

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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.

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

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

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pub fn remote_debug_config(self, input: RemoteDebugConfig) -> Self

Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.

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pub fn set_remote_debug_config(self, input: Option<RemoteDebugConfig>) -> Self

Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.

Source

pub fn get_remote_debug_config(&self) -> &Option<RemoteDebugConfig>

Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.

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pub fn infra_check_config(self, input: InfraCheckConfig) -> Self

Contains information about the infrastructure health check configuration for the training job.

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pub fn set_infra_check_config(self, input: Option<InfraCheckConfig>) -> Self

Contains information about the infrastructure health check configuration for the training job.

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pub fn get_infra_check_config(&self) -> &Option<InfraCheckConfig>

Contains information about the infrastructure health check configuration for the training job.

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pub fn build(self) -> DescribeTrainingJobOutput

Consumes the builder and constructs a DescribeTrainingJobOutput.

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impl Clone for DescribeTrainingJobOutputBuilder

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fn clone(&self) -> DescribeTrainingJobOutputBuilder

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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 DescribeTrainingJobOutputBuilder

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

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fn default() -> DescribeTrainingJobOutputBuilder

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fn eq(&self, other: &DescribeTrainingJobOutputBuilder) -> 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 DescribeTrainingJobOutputBuilder

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