DescribeTrainingJobResult (AWS SDK for Java

java.lang.Object
- com.amazonaws.AmazonWebServiceResult<ResponseMetadata>
- - com.amazonaws.services.sagemaker.model.DescribeTrainingJobResult

All Implemented Interfaces:: Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class DescribeTrainingJobResult
extends AmazonWebServiceResult<ResponseMetadata>
implements Serializable, Cloneable

See Also:: AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

DescribeTrainingJobResult()

Constructors
Constructor and Description
`DescribeTrainingJobResult()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`DescribeTrainingJobResult`	`addHyperParametersEntry(String key, String value)`
`DescribeTrainingJobResult`	`clearHyperParametersEntries()` Removes all the entries added into HyperParameters.
`DescribeTrainingJobResult`	`clone()`
`boolean`	`equals(Object obj)`
`AlgorithmSpecification`	`getAlgorithmSpecification()` Information about the algorithm used for training, and algorithm metadata.
`Date`	`getCreationTime()` A timestamp that indicates when the training job was created.
`Boolean`	`getEnableInterContainerTrafficEncryption()` To encrypt all communications between ML compute instances in distributed training, choose `True`.
`Boolean`	`getEnableNetworkIsolation()` If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose `True`.
`String`	`getFailureReason()` If the training job failed, the reason it failed.
`List<MetricData>`	`getFinalMetricDataList()` A collection of `MetricData` objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
`Map<String,String>`	`getHyperParameters()` Algorithm-specific parameters.
`List<Channel>`	`getInputDataConfig()` An array of `Channel` objects that describes each data input channel.
`String`	`getLabelingJobArn()` The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
`Date`	`getLastModifiedTime()` A timestamp that indicates when the status of the training job was last modified.
`ModelArtifacts`	`getModelArtifacts()` Information about the Amazon S3 location that is configured for storing model artifacts.
`OutputDataConfig`	`getOutputDataConfig()` The S3 path where model artifacts that you configured when creating the job are stored.
`ResourceConfig`	`getResourceConfig()` Resources, including ML compute instances and ML storage volumes, that are configured for model training.
`String`	`getRoleArn()` The AWS Identity and Access Management (IAM) role configured for the training job.
`String`	`getSecondaryStatus()` Provides detailed information about the state of the training job.
`List<SecondaryStatusTransition>`	`getSecondaryStatusTransitions()` A history of all of the secondary statuses that the training job has transitioned through.
`StoppingCondition`	`getStoppingCondition()` Specifies a limit to how long a model training job can run.
`Date`	`getTrainingEndTime()` Indicates the time when the training job ends on training instances.
`String`	`getTrainingJobArn()` The Amazon Resource Name (ARN) of the training job.
`String`	`getTrainingJobName()` Name of the model training job.
`String`	`getTrainingJobStatus()` The status of the training job.
`Date`	`getTrainingStartTime()` Indicates the time when the training job starts on training instances.
`String`	`getTuningJobArn()` The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
`VpcConfig`	`getVpcConfig()` A VpcConfig object that specifies the VPC that this training job has access to.
`int`	`hashCode()`
`Boolean`	`isEnableInterContainerTrafficEncryption()` To encrypt all communications between ML compute instances in distributed training, choose `True`.
`Boolean`	`isEnableNetworkIsolation()` If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose `True`.
`void`	`setAlgorithmSpecification(AlgorithmSpecification algorithmSpecification)` Information about the algorithm used for training, and algorithm metadata.
`void`	`setCreationTime(Date creationTime)` A timestamp that indicates when the training job was created.
`void`	`setEnableInterContainerTrafficEncryption(Boolean enableInterContainerTrafficEncryption)` To encrypt all communications between ML compute instances in distributed training, choose `True`.
`void`	`setEnableNetworkIsolation(Boolean enableNetworkIsolation)` If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose `True`.
`void`	`setFailureReason(String failureReason)` If the training job failed, the reason it failed.
`void`	`setFinalMetricDataList(Collection<MetricData> finalMetricDataList)` A collection of `MetricData` objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
`void`	`setHyperParameters(Map<String,String> hyperParameters)` Algorithm-specific parameters.
`void`	`setInputDataConfig(Collection<Channel> inputDataConfig)` An array of `Channel` objects that describes each data input channel.
`void`	`setLabelingJobArn(String labelingJobArn)` The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
`void`	`setLastModifiedTime(Date lastModifiedTime)` A timestamp that indicates when the status of the training job was last modified.
`void`	`setModelArtifacts(ModelArtifacts modelArtifacts)` Information about the Amazon S3 location that is configured for storing model artifacts.
`void`	`setOutputDataConfig(OutputDataConfig outputDataConfig)` The S3 path where model artifacts that you configured when creating the job are stored.
`void`	`setResourceConfig(ResourceConfig resourceConfig)` Resources, including ML compute instances and ML storage volumes, that are configured for model training.
`void`	`setRoleArn(String roleArn)` The AWS Identity and Access Management (IAM) role configured for the training job.
`void`	`setSecondaryStatus(String secondaryStatus)` Provides detailed information about the state of the training job.
`void`	`setSecondaryStatusTransitions(Collection<SecondaryStatusTransition> secondaryStatusTransitions)` A history of all of the secondary statuses that the training job has transitioned through.
`void`	`setStoppingCondition(StoppingCondition stoppingCondition)` Specifies a limit to how long a model training job can run.
`void`	`setTrainingEndTime(Date trainingEndTime)` Indicates the time when the training job ends on training instances.
`void`	`setTrainingJobArn(String trainingJobArn)` The Amazon Resource Name (ARN) of the training job.
`void`	`setTrainingJobName(String trainingJobName)` Name of the model training job.
`void`	`setTrainingJobStatus(String trainingJobStatus)` The status of the training job.
`void`	`setTrainingStartTime(Date trainingStartTime)` Indicates the time when the training job starts on training instances.
`void`	`setTuningJobArn(String tuningJobArn)` The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
`void`	`setVpcConfig(VpcConfig vpcConfig)` A VpcConfig object that specifies the VPC that this training job has access to.
`String`	`toString()` Returns a string representation of this object.
`DescribeTrainingJobResult`	`withAlgorithmSpecification(AlgorithmSpecification algorithmSpecification)` Information about the algorithm used for training, and algorithm metadata.
`DescribeTrainingJobResult`	`withCreationTime(Date creationTime)` A timestamp that indicates when the training job was created.
`DescribeTrainingJobResult`	`withEnableInterContainerTrafficEncryption(Boolean enableInterContainerTrafficEncryption)` To encrypt all communications between ML compute instances in distributed training, choose `True`.
`DescribeTrainingJobResult`	`withEnableNetworkIsolation(Boolean enableNetworkIsolation)` If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose `True`.
`DescribeTrainingJobResult`	`withFailureReason(String failureReason)` If the training job failed, the reason it failed.
`DescribeTrainingJobResult`	`withFinalMetricDataList(Collection<MetricData> finalMetricDataList)` A collection of `MetricData` objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
`DescribeTrainingJobResult`	`withFinalMetricDataList(MetricData... finalMetricDataList)` A collection of `MetricData` objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
`DescribeTrainingJobResult`	`withHyperParameters(Map<String,String> hyperParameters)` Algorithm-specific parameters.
`DescribeTrainingJobResult`	`withInputDataConfig(Channel... inputDataConfig)` An array of `Channel` objects that describes each data input channel.
`DescribeTrainingJobResult`	`withInputDataConfig(Collection<Channel> inputDataConfig)` An array of `Channel` objects that describes each data input channel.
`DescribeTrainingJobResult`	`withLabelingJobArn(String labelingJobArn)` The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
`DescribeTrainingJobResult`	`withLastModifiedTime(Date lastModifiedTime)` A timestamp that indicates when the status of the training job was last modified.
`DescribeTrainingJobResult`	`withModelArtifacts(ModelArtifacts modelArtifacts)` Information about the Amazon S3 location that is configured for storing model artifacts.
`DescribeTrainingJobResult`	`withOutputDataConfig(OutputDataConfig outputDataConfig)` The S3 path where model artifacts that you configured when creating the job are stored.
`DescribeTrainingJobResult`	`withResourceConfig(ResourceConfig resourceConfig)` Resources, including ML compute instances and ML storage volumes, that are configured for model training.
`DescribeTrainingJobResult`	`withRoleArn(String roleArn)` The AWS Identity and Access Management (IAM) role configured for the training job.
`DescribeTrainingJobResult`	`withSecondaryStatus(SecondaryStatus secondaryStatus)` Provides detailed information about the state of the training job.
`DescribeTrainingJobResult`	`withSecondaryStatus(String secondaryStatus)` Provides detailed information about the state of the training job.
`DescribeTrainingJobResult`	`withSecondaryStatusTransitions(Collection<SecondaryStatusTransition> secondaryStatusTransitions)` A history of all of the secondary statuses that the training job has transitioned through.
`DescribeTrainingJobResult`	`withSecondaryStatusTransitions(SecondaryStatusTransition... secondaryStatusTransitions)` A history of all of the secondary statuses that the training job has transitioned through.
`DescribeTrainingJobResult`	`withStoppingCondition(StoppingCondition stoppingCondition)` Specifies a limit to how long a model training job can run.
`DescribeTrainingJobResult`	`withTrainingEndTime(Date trainingEndTime)` Indicates the time when the training job ends on training instances.
`DescribeTrainingJobResult`	`withTrainingJobArn(String trainingJobArn)` The Amazon Resource Name (ARN) of the training job.
`DescribeTrainingJobResult`	`withTrainingJobName(String trainingJobName)` Name of the model training job.
`DescribeTrainingJobResult`	`withTrainingJobStatus(String trainingJobStatus)` The status of the training job.
`DescribeTrainingJobResult`	`withTrainingJobStatus(TrainingJobStatus trainingJobStatus)` The status of the training job.
`DescribeTrainingJobResult`	`withTrainingStartTime(Date trainingStartTime)` Indicates the time when the training job starts on training instances.
`DescribeTrainingJobResult`	`withTuningJobArn(String tuningJobArn)` The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
`DescribeTrainingJobResult`	`withVpcConfig(VpcConfig vpcConfig)` A VpcConfig object that specifies the VPC that this training job has access to.

Methods inherited from class com.amazonaws.AmazonWebServiceResult
getSdkHttpMetadata, getSdkResponseMetadata, setSdkHttpMetadata, setSdkResponseMetadata

Methods inherited from class java.lang.Object
getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - DescribeTrainingJobResult
```
public DescribeTrainingJobResult()
```
- Method Detail
  - setTrainingJobName
```
public void setTrainingJobName(String trainingJobName)
```
    Name of the model training job.
    
    Parameters:
    
    trainingJobName - Name of the model training job.
  - getTrainingJobName
```
public String getTrainingJobName()
```
    Name of the model training job.
    
    Returns:
    
    Name of the model training job.
  - withTrainingJobName
```
public DescribeTrainingJobResult withTrainingJobName(String trainingJobName)
```
    Name of the model training job.
    
    Parameters:
    
    trainingJobName - Name of the model training job.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setTrainingJobArn
```
public void setTrainingJobArn(String trainingJobArn)
```
    The Amazon Resource Name (ARN) of the training job.
    
    Parameters:
    
    trainingJobArn - The Amazon Resource Name (ARN) of the training job.
  - getTrainingJobArn
```
public String getTrainingJobArn()
```
    The Amazon Resource Name (ARN) of the training job.
    
    Returns:
    
    The Amazon Resource Name (ARN) of the training job.
  - withTrainingJobArn
```
public DescribeTrainingJobResult withTrainingJobArn(String trainingJobArn)
```
    The Amazon Resource Name (ARN) of the training job.
    
    Parameters:
    
    trainingJobArn - The Amazon Resource Name (ARN) of the training job.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setTuningJobArn
```
public void setTuningJobArn(String tuningJobArn)
```
    The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
    
    Parameters:
    
    tuningJobArn - The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
  - getTuningJobArn
```
public String getTuningJobArn()
```
    The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
    
    Returns:
    
    The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
  - withTuningJobArn
```
public DescribeTrainingJobResult withTuningJobArn(String tuningJobArn)
```
    The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
    
    Parameters:
    
    tuningJobArn - The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setLabelingJobArn
```
public void setLabelingJobArn(String labelingJobArn)
```
    The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
    
    Parameters:
    
    labelingJobArn - The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
  - getLabelingJobArn
```
public String getLabelingJobArn()
```
    The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
    
    Returns:
    
    The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
  - withLabelingJobArn
```
public DescribeTrainingJobResult withLabelingJobArn(String labelingJobArn)
```
    The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
    
    Parameters:
    
    labelingJobArn - The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setModelArtifacts
```
public void setModelArtifacts(ModelArtifacts modelArtifacts)
```
    Information about the Amazon S3 location that is configured for storing model artifacts.
    
    Parameters:
    
    modelArtifacts - Information about the Amazon S3 location that is configured for storing model artifacts.
  - getModelArtifacts
```
public ModelArtifacts getModelArtifacts()
```
    Information about the Amazon S3 location that is configured for storing model artifacts.
    
    Returns:
    
    Information about the Amazon S3 location that is configured for storing model artifacts.
  - withModelArtifacts
```
public DescribeTrainingJobResult withModelArtifacts(ModelArtifacts modelArtifacts)
```
    Information about the Amazon S3 location that is configured for storing model artifacts.
    
    Parameters:
    
    modelArtifacts - Information about the Amazon S3 location that is configured for storing model artifacts.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setTrainingJobStatus
```
public void setTrainingJobStatus(String trainingJobStatus)
```
    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.
    Parameters:
    trainingJobStatus - 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.
    See Also:
    
    TrainingJobStatus
  - getTrainingJobStatus
```
public String getTrainingJobStatus()
```
    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.
    Returns:
    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.
    See Also:
    
    TrainingJobStatus
  - withTrainingJobStatus
```
public DescribeTrainingJobResult withTrainingJobStatus(String trainingJobStatus)
```
    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.
    Parameters:
    trainingJobStatus - 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.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    TrainingJobStatus
  - withTrainingJobStatus
```
public DescribeTrainingJobResult withTrainingJobStatus(TrainingJobStatus trainingJobStatus)
```
    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.
    Parameters:
    trainingJobStatus - 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.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    TrainingJobStatus
  - setSecondaryStatus
```
public void setSecondaryStatus(String 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.
    
    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
    Parameters:
    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.
    
    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
    See Also:
    
    SecondaryStatus
  - getSecondaryStatus
```
public String getSecondaryStatus()
```
    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.
    
    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
    Returns:
    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.
    
    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
    See Also:
    
    SecondaryStatus
  - withSecondaryStatus
```
public DescribeTrainingJobResult withSecondaryStatus(String 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.
    
    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
    Parameters:
    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.
    
    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
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    SecondaryStatus
  - withSecondaryStatus
```
public DescribeTrainingJobResult withSecondaryStatus(SecondaryStatus 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.
    
    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
    Parameters:
    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.
    
    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
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    SecondaryStatus
  - setFailureReason
```
public void setFailureReason(String failureReason)
```
    If the training job failed, the reason it failed.
    
    Parameters:
    
    failureReason - If the training job failed, the reason it failed.
  - getFailureReason
```
public String getFailureReason()
```
    If the training job failed, the reason it failed.
    
    Returns:
    
    If the training job failed, the reason it failed.
  - withFailureReason
```
public DescribeTrainingJobResult withFailureReason(String failureReason)
```
    If the training job failed, the reason it failed.
    
    Parameters:
    
    failureReason - If the training job failed, the reason it failed.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getHyperParameters
```
public Map<String,String> getHyperParameters()
```
    Algorithm-specific parameters.
    
    Returns:
    
    Algorithm-specific parameters.
  - setHyperParameters
```
public void setHyperParameters(Map<String,String> hyperParameters)
```
    Algorithm-specific parameters.
    
    Parameters:
    
    hyperParameters - Algorithm-specific parameters.
  - withHyperParameters
```
public DescribeTrainingJobResult withHyperParameters(Map<String,String> hyperParameters)
```
    Algorithm-specific parameters.
    
    Parameters:
    
    hyperParameters - Algorithm-specific parameters.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - addHyperParametersEntry
```
public DescribeTrainingJobResult addHyperParametersEntry(String key,
                                                         String value)
```
  - clearHyperParametersEntries
```
public DescribeTrainingJobResult clearHyperParametersEntries()
```
    Removes all the entries added into HyperParameters.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setAlgorithmSpecification
```
public void setAlgorithmSpecification(AlgorithmSpecification algorithmSpecification)
```
    Information about the algorithm used for training, and algorithm metadata.
    
    Parameters:
    
    algorithmSpecification - Information about the algorithm used for training, and algorithm metadata.
  - getAlgorithmSpecification
```
public AlgorithmSpecification getAlgorithmSpecification()
```
    Information about the algorithm used for training, and algorithm metadata.
    
    Returns:
    
    Information about the algorithm used for training, and algorithm metadata.
  - withAlgorithmSpecification
```
public DescribeTrainingJobResult withAlgorithmSpecification(AlgorithmSpecification algorithmSpecification)
```
    Information about the algorithm used for training, and algorithm metadata.
    
    Parameters:
    
    algorithmSpecification - Information about the algorithm used for training, and algorithm metadata.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setRoleArn
```
public void setRoleArn(String roleArn)
```
    The AWS Identity and Access Management (IAM) role configured for the training job.
    
    Parameters:
    
    roleArn - The AWS Identity and Access Management (IAM) role configured for the training job.
  - getRoleArn
```
public String getRoleArn()
```
    The AWS Identity and Access Management (IAM) role configured for the training job.
    
    Returns:
    
    The AWS Identity and Access Management (IAM) role configured for the training job.
  - withRoleArn
```
public DescribeTrainingJobResult withRoleArn(String roleArn)
```
    The AWS Identity and Access Management (IAM) role configured for the training job.
    
    Parameters:
    
    roleArn - The AWS Identity and Access Management (IAM) role configured for the training job.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getInputDataConfig
```
public List<Channel> getInputDataConfig()
```
    An array of Channel objects that describes each data input channel.
    
    Returns:
    
    An array of Channel objects that describes each data input channel.
  - setInputDataConfig
```
public void setInputDataConfig(Collection<Channel> inputDataConfig)
```
    An array of Channel objects that describes each data input channel.
    
    Parameters:
    
    inputDataConfig - An array of Channel objects that describes each data input channel.
  - withInputDataConfig
```
public DescribeTrainingJobResult withInputDataConfig(Channel... inputDataConfig)
```
    An array of Channel objects that describes each data input channel.
    
    NOTE: This method appends the values to the existing list (if any). Use setInputDataConfig(java.util.Collection) or withInputDataConfig(java.util.Collection) if you want to override the existing values.
    
    Parameters:
    
    inputDataConfig - An array of Channel objects that describes each data input channel.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - withInputDataConfig
```
public DescribeTrainingJobResult withInputDataConfig(Collection<Channel> inputDataConfig)
```
    An array of Channel objects that describes each data input channel.
    
    Parameters:
    
    inputDataConfig - An array of Channel objects that describes each data input channel.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setOutputDataConfig
```
public void setOutputDataConfig(OutputDataConfig outputDataConfig)
```
    The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
    
    Parameters:
    
    outputDataConfig - The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
  - getOutputDataConfig
```
public OutputDataConfig getOutputDataConfig()
```
    The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
    
    Returns:
    
    The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
  - withOutputDataConfig
```
public DescribeTrainingJobResult withOutputDataConfig(OutputDataConfig outputDataConfig)
```
    The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
    
    Parameters:
    
    outputDataConfig - The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setResourceConfig
```
public void setResourceConfig(ResourceConfig resourceConfig)
```
    Resources, including ML compute instances and ML storage volumes, that are configured for model training.
    
    Parameters:
    
    resourceConfig - Resources, including ML compute instances and ML storage volumes, that are configured for model training.
  - getResourceConfig
```
public ResourceConfig getResourceConfig()
```
    Resources, including ML compute instances and ML storage volumes, that are configured for model training.
    
    Returns:
    
    Resources, including ML compute instances and ML storage volumes, that are configured for model training.
  - withResourceConfig
```
public DescribeTrainingJobResult withResourceConfig(ResourceConfig resourceConfig)
```
    Resources, including ML compute instances and ML storage volumes, that are configured for model training.
    
    Parameters:
    
    resourceConfig - Resources, including ML compute instances and ML storage volumes, that are configured for model training.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setVpcConfig
```
public void setVpcConfig(VpcConfig 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.
    
    Parameters:
    
    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.
  - getVpcConfig
```
public VpcConfig getVpcConfig()
```
    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.
    
    Returns:
    
    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.
  - withVpcConfig
```
public DescribeTrainingJobResult withVpcConfig(VpcConfig 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.
    
    Parameters:
    
    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.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setStoppingCondition
```
public void setStoppingCondition(StoppingCondition stoppingCondition)
```
    Specifies a limit to how long a model training job can run. 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.
    
    Parameters:
    
    stoppingCondition - Specifies a limit to how long a model training job can run. 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.
  - getStoppingCondition
```
public StoppingCondition getStoppingCondition()
```
    Specifies a limit to how long a model training job can run. 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.
    
    Returns:
    
    Specifies a limit to how long a model training job can run. 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.
  - withStoppingCondition
```
public DescribeTrainingJobResult withStoppingCondition(StoppingCondition stoppingCondition)
```
    Specifies a limit to how long a model training job can run. 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.
    
    Parameters:
    
    stoppingCondition - Specifies a limit to how long a model training job can run. 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.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setCreationTime
```
public void setCreationTime(Date creationTime)
```
    A timestamp that indicates when the training job was created.
    
    Parameters:
    
    creationTime - A timestamp that indicates when the training job was created.
  - getCreationTime
```
public Date getCreationTime()
```
    A timestamp that indicates when the training job was created.
    
    Returns:
    
    A timestamp that indicates when the training job was created.
  - withCreationTime
```
public DescribeTrainingJobResult withCreationTime(Date creationTime)
```
    A timestamp that indicates when the training job was created.
    
    Parameters:
    
    creationTime - A timestamp that indicates when the training job was created.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setTrainingStartTime
```
public void setTrainingStartTime(Date trainingStartTime)
```
    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.
    
    Parameters:
    
    trainingStartTime - 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.
  - getTrainingStartTime
```
public Date getTrainingStartTime()
```
    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.
    
    Returns:
    
    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.
  - withTrainingStartTime
```
public DescribeTrainingJobResult withTrainingStartTime(Date trainingStartTime)
```
    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.
    
    Parameters:
    
    trainingStartTime - 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.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setTrainingEndTime
```
public void setTrainingEndTime(Date trainingEndTime)
```
    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.
    
    Parameters:
    
    trainingEndTime - 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.
  - getTrainingEndTime
```
public Date getTrainingEndTime()
```
    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.
    
    Returns:
    
    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.
  - withTrainingEndTime
```
public DescribeTrainingJobResult withTrainingEndTime(Date trainingEndTime)
```
    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.
    
    Parameters:
    
    trainingEndTime - 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.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setLastModifiedTime
```
public void setLastModifiedTime(Date lastModifiedTime)
```
    A timestamp that indicates when the status of the training job was last modified.
    
    Parameters:
    
    lastModifiedTime - A timestamp that indicates when the status of the training job was last modified.
  - getLastModifiedTime
```
public Date getLastModifiedTime()
```
    A timestamp that indicates when the status of the training job was last modified.
    
    Returns:
    
    A timestamp that indicates when the status of the training job was last modified.
  - withLastModifiedTime
```
public DescribeTrainingJobResult withLastModifiedTime(Date lastModifiedTime)
```
    A timestamp that indicates when the status of the training job was last modified.
    
    Parameters:
    
    lastModifiedTime - A timestamp that indicates when the status of the training job was last modified.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getSecondaryStatusTransitions
```
public List<SecondaryStatusTransition> getSecondaryStatusTransitions()
```
    A history of all of the secondary statuses that the training job has transitioned through.
    
    Returns:
    
    A history of all of the secondary statuses that the training job has transitioned through.
  - setSecondaryStatusTransitions
```
public void setSecondaryStatusTransitions(Collection<SecondaryStatusTransition> secondaryStatusTransitions)
```
    A history of all of the secondary statuses that the training job has transitioned through.
    
    Parameters:
    
    secondaryStatusTransitions - A history of all of the secondary statuses that the training job has transitioned through.
  - withSecondaryStatusTransitions
```
public DescribeTrainingJobResult withSecondaryStatusTransitions(SecondaryStatusTransition... secondaryStatusTransitions)
```
    A history of all of the secondary statuses that the training job has transitioned through.
    
    NOTE: This method appends the values to the existing list (if any). Use setSecondaryStatusTransitions(java.util.Collection) or withSecondaryStatusTransitions(java.util.Collection) if you want to override the existing values.
    
    Parameters:
    
    secondaryStatusTransitions - A history of all of the secondary statuses that the training job has transitioned through.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - withSecondaryStatusTransitions
```
public DescribeTrainingJobResult withSecondaryStatusTransitions(Collection<SecondaryStatusTransition> secondaryStatusTransitions)
```
    A history of all of the secondary statuses that the training job has transitioned through.
    
    Parameters:
    
    secondaryStatusTransitions - A history of all of the secondary statuses that the training job has transitioned through.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getFinalMetricDataList
```
public List<MetricData> getFinalMetricDataList()
```
    A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
    
    Returns:
    
    A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
  - setFinalMetricDataList
```
public void setFinalMetricDataList(Collection<MetricData> finalMetricDataList)
```
    A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
    
    Parameters:
    
    finalMetricDataList - A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
  - withFinalMetricDataList
```
public DescribeTrainingJobResult withFinalMetricDataList(MetricData... finalMetricDataList)
```
    A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
    
    NOTE: This method appends the values to the existing list (if any). Use setFinalMetricDataList(java.util.Collection) or withFinalMetricDataList(java.util.Collection) if you want to override the existing values.
    
    Parameters:
    
    finalMetricDataList - A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - withFinalMetricDataList
```
public DescribeTrainingJobResult withFinalMetricDataList(Collection<MetricData> finalMetricDataList)
```
    A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
    
    Parameters:
    
    finalMetricDataList - A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setEnableNetworkIsolation
```
public void setEnableNetworkIsolation(Boolean enableNetworkIsolation)
```
    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.
    
    The Semantic Segmentation built-in algorithm does not support network isolation.
    
    Parameters:
    
    enableNetworkIsolation - 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.
    
    The Semantic Segmentation built-in algorithm does not support network isolation.
  - getEnableNetworkIsolation
```
public Boolean getEnableNetworkIsolation()
```
    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.
    
    The Semantic Segmentation built-in algorithm does not support network isolation.
    
    Returns:
    
    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.
    
    The Semantic Segmentation built-in algorithm does not support network isolation.
  - withEnableNetworkIsolation
```
public DescribeTrainingJobResult withEnableNetworkIsolation(Boolean enableNetworkIsolation)
```
    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.
    
    The Semantic Segmentation built-in algorithm does not support network isolation.
    
    Parameters:
    
    enableNetworkIsolation - 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.
    
    The Semantic Segmentation built-in algorithm does not support network isolation.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - isEnableNetworkIsolation
```
public Boolean isEnableNetworkIsolation()
```
    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.
    
    The Semantic Segmentation built-in algorithm does not support network isolation.
    
    Returns:
    
    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.
    
    The Semantic Segmentation built-in algorithm does not support network isolation.
  - setEnableInterContainerTrafficEncryption
```
public void setEnableInterContainerTrafficEncryption(Boolean enableInterContainerTrafficEncryption)
```
    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.
    
    Parameters:
    
    enableInterContainerTrafficEncryption - 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.
  - getEnableInterContainerTrafficEncryption
```
public Boolean getEnableInterContainerTrafficEncryption()
```
    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.
    
    Returns:
    
    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.
  - withEnableInterContainerTrafficEncryption
```
public DescribeTrainingJobResult withEnableInterContainerTrafficEncryption(Boolean enableInterContainerTrafficEncryption)
```
    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.
    
    Parameters:
    
    enableInterContainerTrafficEncryption - 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.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - isEnableInterContainerTrafficEncryption
```
public Boolean isEnableInterContainerTrafficEncryption()
```
    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.
    
    Returns:
    
    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.
  - toString
```
public String toString()
```
    Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    A string representation of this object.
    
    See Also:
    
    Object.toString()
  - equals
```
public boolean equals(Object obj)
```
    Overrides:
    
    equals in class Object
  - hashCode
```
public int hashCode()
```
    Overrides:
    
    hashCode in class Object
  - clone
```
public DescribeTrainingJobResult clone()
```
    Overrides:
    
    clone in class Object

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Class DescribeTrainingJobResult

Constructor Summary

Method Summary

Methods inherited from class com.amazonaws.AmazonWebServiceResult

Methods inherited from class java.lang.Object

Constructor Detail

DescribeTrainingJobResult

Method Detail

setTrainingJobName

getTrainingJobName

withTrainingJobName

setTrainingJobArn

getTrainingJobArn

withTrainingJobArn

setTuningJobArn

getTuningJobArn

withTuningJobArn

setLabelingJobArn

getLabelingJobArn

withLabelingJobArn

setModelArtifacts

getModelArtifacts

withModelArtifacts

setTrainingJobStatus

getTrainingJobStatus

withTrainingJobStatus

withTrainingJobStatus

setSecondaryStatus

getSecondaryStatus

withSecondaryStatus

withSecondaryStatus

setFailureReason

getFailureReason

withFailureReason

getHyperParameters

setHyperParameters

withHyperParameters

addHyperParametersEntry

clearHyperParametersEntries

setAlgorithmSpecification

getAlgorithmSpecification

withAlgorithmSpecification

setRoleArn

getRoleArn

withRoleArn

getInputDataConfig

setInputDataConfig

withInputDataConfig

withInputDataConfig

setOutputDataConfig

getOutputDataConfig

withOutputDataConfig

setResourceConfig

getResourceConfig

withResourceConfig

setVpcConfig

getVpcConfig

withVpcConfig

setStoppingCondition

getStoppingCondition

withStoppingCondition

setCreationTime

getCreationTime

withCreationTime

setTrainingStartTime

getTrainingStartTime

withTrainingStartTime

setTrainingEndTime

getTrainingEndTime

withTrainingEndTime

setLastModifiedTime

getLastModifiedTime

withLastModifiedTime

getSecondaryStatusTransitions

setSecondaryStatusTransitions

withSecondaryStatusTransitions

withSecondaryStatusTransitions

getFinalMetricDataList

setFinalMetricDataList