REQUIRED AlgorithmSpecification => Paws::SageMaker::AlgorithmSpecification

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

AutoMLJobArn => Str

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

BillableTimeInSeconds => Int

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 Amazon 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%.

CheckpointConfig => Paws::SageMaker::CheckpointConfig

REQUIRED CreationTime => Str

A timestamp that indicates when the training job was created.

DebugHookConfig => Paws::SageMaker::DebugHookConfig

DebugRuleConfigurations => ArrayRef[Paws::SageMaker::DebugRuleConfiguration]

Configuration information for Debugger rules for debugging output tensors.

DebugRuleEvaluationStatuses => ArrayRef[Paws::SageMaker::DebugRuleEvaluationStatus]

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

EnableInterContainerTrafficEncryption => 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.

EnableManagedSpotTraining => Bool

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

EnableNetworkIsolation => 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.

Environment => Paws::SageMaker::TrainingEnvironmentMap

The environment variables to set in the Docker container.

ExperimentConfig => Paws::SageMaker::ExperimentConfig

FailureReason => Str

If the training job failed, the reason it failed.

FinalMetricDataList => ArrayRef[Paws::SageMaker::MetricData]

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

HyperParameters => Paws::SageMaker::HyperParameters

Algorithm-specific parameters.

InputDataConfig => ArrayRef[Paws::SageMaker::Channel]

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

LabelingJobArn => Str

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

LastModifiedTime => Str

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

REQUIRED ModelArtifacts => Paws::SageMaker::ModelArtifacts

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

OutputDataConfig => Paws::SageMaker::OutputDataConfig

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

ProfilerConfig => Paws::SageMaker::ProfilerConfig

ProfilerRuleConfigurations => ArrayRef[Paws::SageMaker::ProfilerRuleConfiguration]

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

ProfilerRuleEvaluationStatuses => ArrayRef[Paws::SageMaker::ProfilerRuleEvaluationStatus]

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

ProfilingStatus => Str

Profiling status of a training job.

Valid values are: "Enabled", "Disabled" =head2 REQUIRED ResourceConfig => Paws::SageMaker::ResourceConfig

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

RetryStrategy => Paws::SageMaker::RetryStrategy

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

RoleArn => Str

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

REQUIRED SecondaryStatus => Str

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

Completed

Failed

Stopped

Stopping

Valid values for "SecondaryStatus" are subject to change.

We no longer support the following secondary statuses:

• "LaunchingMLInstances"
• "PreparingTraining"
• "DownloadingTrainingImage"

Valid values are: "Starting", "LaunchingMLInstances", "PreparingTrainingStack", "Downloading", "DownloadingTrainingImage", "Training", "Uploading", "Stopping", "Stopped", "MaxRuntimeExceeded", "Completed", "Failed", "Interrupted", "MaxWaitTimeExceeded", "Updating", "Restarting" =head2 SecondaryStatusTransitions => ArrayRef[Paws::SageMaker::SecondaryStatusTransition]

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

REQUIRED StoppingCondition => Paws::SageMaker::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.

TensorBoardOutputConfig => Paws::SageMaker::TensorBoardOutputConfig

TrainingEndTime => Str

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.

REQUIRED TrainingJobArn => Str

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

REQUIRED TrainingJobName => Str

Name of the model training job.

REQUIRED TrainingJobStatus => Str

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".

Valid values are: "InProgress", "Completed", "Failed", "Stopping", "Stopped" =head2 TrainingStartTime => Str

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.

TrainingTimeInSeconds => Int

The training time in seconds.

TuningJobArn => Str

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

VpcConfig => Paws::SageMaker::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 (https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html).

_request_id => Str