class Aws::SageMaker::Types::DescribeTrainingJobResponse

@!attribute [rw] training_job_name

Name of the model training job.
@return [String]

@!attribute [rw] training_job_arn

The Amazon Resource Name (ARN) of the training job.
@return [String]

@!attribute [rw] tuning_job_arn

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

@!attribute [rw] labeling_job_arn

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

@!attribute [rw] auto_ml_job_arn

The Amazon Resource Name (ARN) of an AutoML job.
@return [String]

@!attribute [rw] model_artifacts

Information about the Amazon S3 location that is configured for
storing model artifacts.
@return [Types::ModelArtifacts]

@!attribute [rw] training_job_status

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`.
@return [String]

@!attribute [rw] secondary_status

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

Amazon SageMaker provides primary statuses and secondary statuses
that apply to each of them:

InProgress
: * `Starting` - Starting the training job.

  * `Downloading` - An optional stage for algorithms that support
    `File` training input mode. It indicates that data is being
    downloaded to the ML storage volumes.

  * `Training` - Training is in progress.

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

  * `Uploading` - Training is complete and the model artifacts are
    being uploaded to the S3 location.

Completed
: * `Completed` - The training job has completed.

  ^

Failed
: * `Failed` - The training job has failed. The reason for the
    failure is returned in the `FailureReason` field of
    `DescribeTrainingJobResponse`.

  ^

Stopped
: * `MaxRuntimeExceeded` - The job stopped because it exceeded the
    maximum allowed runtime.

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

  * `Stopped` - The training job has stopped.

Stopping
: * `Stopping` - Stopping the training job.

  ^

Valid values for `SecondaryStatus` are subject to change.

We no longer support the following secondary statuses:

* `LaunchingMLInstances`

* `PreparingTraining`

* `DownloadingTrainingImage`
@return [String]

@!attribute [rw] failure_reason

If the training job failed, the reason it failed.
@return [String]

@!attribute [rw] hyper_parameters

Algorithm-specific parameters.
@return [Hash<String,String>]

@!attribute [rw] algorithm_specification

Information about the algorithm used for training, and algorithm
metadata.
@return [Types::AlgorithmSpecification]

@!attribute [rw] role_arn

The Amazon Web Services Identity and Access Management (IAM) role
configured for the training job.
@return [String]

@!attribute [rw] input_data_config

An array of `Channel` objects that describes each data input
channel.
@return [Array<Types::Channel>]

@!attribute [rw] output_data_config

The S3 path where model artifacts that you configured when creating
the job are stored. Amazon SageMaker creates subfolders for model
artifacts.
@return [Types::OutputDataConfig]

@!attribute [rw] resource_config

Resources, including ML compute instances and ML storage volumes,
that are configured for model training.
@return [Types::ResourceConfig]

@!attribute [rw] vpc_config

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][1].

[1]: https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html
@return [Types::VpcConfig]

@!attribute [rw] stopping_condition

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.
@return [Types::StoppingCondition]

@!attribute [rw] creation_time

A timestamp that indicates when the training job was created.
@return [Time]

@!attribute [rw] training_start_time

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.
@return [Time]

@!attribute [rw] training_end_time

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.
@return [Time]

@!attribute [rw] last_modified_time

A timestamp that indicates when the status of the training job was
last modified.
@return [Time]

@!attribute [rw] secondary_status_transitions

A history of all of the secondary statuses that the training job has
transitioned through.
@return [Array<Types::SecondaryStatusTransition>]

@!attribute [rw] 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.
@return [Array<Types::MetricData>]

@!attribute [rw] enable_network_isolation

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.
@return [Boolean]

@!attribute [rw] enable_inter_container_traffic_encryption

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.
@return [Boolean]

@!attribute [rw] enable_managed_spot_training

A Boolean indicating whether managed spot training is enabled
(`True`) or not (`False`).
@return [Boolean]

@!attribute [rw] checkpoint_config

Contains information about the output location for managed spot
training checkpoint data.
@return [Types::CheckpointConfig]

@!attribute [rw] training_time_in_seconds

The training time in seconds.
@return [Integer]

@!attribute [rw] billable_time_in_seconds

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%.
@return [Integer]

@!attribute [rw] debug_hook_config

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][1].

[1]: https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html
@return [Types::DebugHookConfig]

@!attribute [rw] experiment_config

Associates a SageMaker job as a trial component with an experiment
and trial. Specified when you call the following APIs:

* CreateProcessingJob

* CreateTrainingJob

* CreateTransformJob
@return [Types::ExperimentConfig]

@!attribute [rw] debug_rule_configurations

Configuration information for Debugger rules for debugging output
tensors.
@return [Array<Types::DebugRuleConfiguration>]

@!attribute [rw] tensor_board_output_config

Configuration of storage locations for the Debugger TensorBoard
output data.
@return [Types::TensorBoardOutputConfig]

@!attribute [rw] debug_rule_evaluation_statuses

Evaluation status of Debugger rules for debugging on a training job.
@return [Array<Types::DebugRuleEvaluationStatus>]

@!attribute [rw] profiler_config

Configuration information for Debugger system monitoring, framework
profiling, and storage paths.
@return [Types::ProfilerConfig]

@!attribute [rw] profiler_rule_configurations

Configuration information for Debugger rules for profiling system
and framework metrics.
@return [Array<Types::ProfilerRuleConfiguration>]

@!attribute [rw] profiler_rule_evaluation_statuses

Evaluation status of Debugger rules for profiling on a training job.
@return [Array<Types::ProfilerRuleEvaluationStatus>]

@!attribute [rw] profiling_status

Profiling status of a training job.
@return [String]

@!attribute [rw] retry_strategy

The number of times to retry the job when the job fails due to an
`InternalServerError`.
@return [Types::RetryStrategy]

@!attribute [rw] environment

The environment variables to set in the Docker container.
@return [Hash<String,String>]

@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/DescribeTrainingJobResponse AWS API Documentation

Constants

SENSITIVE