Post x amz targetsagemaker.describetrainingjob

v1

latestOpenAPI 3.0.0Apache 2.0 License2026-07-143021,9861.6 MB

Returns information about a training job.

Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason, attributes like TrainingStartTime, TrainingTimeInSeconds, TrainingEndTime, and BillableTimeInSeconds may not be present in the response.

post/#X-Amz-Target=SageMaker.DescribeTrainingJob

Headers

X-Amz-Target'SageMaker.DescribeTrainingJob' required

Request body

TrainingJobNamestring required

The name of the training job.

Response

Success

TrainingJobNamestring required

Name of the model training job.

TrainingJobArnstring required

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

TuningJobArnstring

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

LabelingJobArnstring

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

AutoMLJobArnstring

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

TrainingJobStatus'InProgress' | 'Completed' | 'Failed' | 'Stopping' | 'Stopped' required

The status of the training job. SageMaker provides the following training job statuses: <ul> <li> <code>InProgress</code> - The training is in progress. </li> <li> <code>Completed</code> - The training job has completed. </li> <li> <code>Failed</code> - The training job has failed. To see the reason for the failure, see the <code>FailureReason</code> field in the response to a <code>DescribeTrainingJobResponse</code> call. </li> <li> <code>Stopping</code> - The training job is stopping. </li> <li> <code>Stopped</code> - The training job has stopped. </li> </ul> For more detailed information, see <code>SecondaryStatus</code>.

Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see <code>StatusMessage</code> under <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_SecondaryStatusTransition.html">SecondaryStatusTransition</a>. SageMaker provides primary statuses and secondary statuses that apply to each of them: <dl> <dt>InProgress</dt> <dd> <ul> <li> <code>Starting</code> - Starting the training job. </li> <li> <code>Downloading</code> - An optional stage for algorithms that support <code>File</code> training input mode. It indicates that data is being downloaded to the ML storage volumes. </li> <li> <code>Training</code> - Training is in progress. </li> <li> <code>Interrupted</code> - The job stopped because the managed spot training instances were interrupted. </li> <li> <code>Uploading</code> - Training is complete and the model artifacts are being uploaded to the S3 location. </li> </ul> </dd> <dt>Completed</dt> <dd> <ul> <li> <code>Completed</code> - The training job has completed. </li> </ul> </dd> <dt>Failed</dt> <dd> <ul> <li> <code>Failed</code> - The training job has failed. The reason for the failure is returned in the <code>FailureReason</code> field of <code>DescribeTrainingJobResponse</code>. </li> </ul> </dd> <dt>Stopped</dt> <dd> <ul> <li> <code>MaxRuntimeExceeded</code> - The job stopped because it exceeded the maximum allowed runtime. </li> <li> <code>MaxWaitTimeExceeded</code> - The job stopped because it exceeded the maximum allowed wait time. </li> <li> <code>Stopped</code> - The training job has stopped. </li> </ul> </dd> <dt>Stopping</dt> <dd> <ul> <li> <code>Stopping</code> - Stopping the training job. </li> </ul> </dd> </dl> <important> Valid values for <code>SecondaryStatus</code> are subject to change. </important> We no longer support the following secondary statuses: <ul> <li> <code>LaunchingMLInstances</code> </li> <li> <code>PreparingTraining</code> </li> <li> <code>DownloadingTrainingImage</code> </li> </ul>

FailureReasonstring

If the training job failed, the reason it failed.

HyperParametersobject

Algorithm-specific parameters.

RoleArnstring

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

CreationTimestring date-time required

A timestamp that indicates when the training job was created.

TrainingStartTimestring date-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 <code>TrainingEndTime</code>. 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.

TrainingEndTimestring date-time

Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of <code>TrainingStartTime</code> 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.

LastModifiedTimestring date-time

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

EnableNetworkIsolationboolean

If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose <code>True</code>. 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.

EnableInterContainerTrafficEncryptionboolean

To encrypt all communications between ML compute instances in distributed training, choose <code>True</code>. 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.

EnableManagedSpotTrainingboolean

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

TrainingTimeInSecondsinteger

The training time in seconds.

BillableTimeInSecondsinteger

The billable time in seconds. Billable time refers to the absolute wall-clock time. Multiply <code>BillableTimeInSeconds</code> by the number of instances (<code>InstanceCount</code>) in your training cluster to get the total compute time SageMaker bills you if you run distributed training. The formula is as follows: <code>BillableTimeInSeconds * InstanceCount</code> . You can calculate the savings from using managed spot training using the formula <code>(1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100</code>. For example, if <code>BillableTimeInSeconds</code> is 100 and <code>TrainingTimeInSeconds</code> is 500, the savings is 80%.

ProfilingStatus'Enabled' | 'Disabled'

Profiling status of a training job.

Environmentobject

The environment variables to set in the Docker container.