Package software.amazon.awssdk.services.sagemaker.model

Class DescribeTrainingJobResponse

    • Method Detail

      • trainingJobName

        public final String trainingJobName()

        Name of the model training job.

        Returns:
        Name of the model training job.

      • trainingJobArn

        public final String trainingJobArn()

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

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

      • tuningJobArn

        public final String tuningJobArn()

        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.

      • labelingJobArn

        public final String labelingJobArn()

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

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

      • autoMLJobArn

        public final String autoMLJobArn()

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

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

      • modelArtifacts

        public final ModelArtifacts modelArtifacts()

        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.

      • trainingJobStatus

        public final TrainingJobStatus trainingJobStatus()

        The status of the training job.

        SageMaker provides the following training job statuses:

        • InProgress - The training is in progress.

        • Completed - The training job has completed.

        • Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.

        • Stopping - The training job is stopping.

        • Stopped - The training job has stopped.

        For more detailed information, see SecondaryStatus.

        If the service returns an enum value that is not available in the current SDK version, trainingJobStatus will return TrainingJobStatus.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from trainingJobStatusAsString().

        Returns:
        The status of the training job.

        SageMaker provides the following training job statuses:

        • InProgress - The training is in progress.

        • Completed - The training job has completed.

        • Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.

        • Stopping - The training job is stopping.

        • Stopped - The training job has stopped.

        For more detailed information, see SecondaryStatus.

        See Also:
        TrainingJobStatus

      • trainingJobStatusAsString

        public final String trainingJobStatusAsString()

        The status of the training job.

        SageMaker provides the following training job statuses:

        • InProgress - The training is in progress.

        • Completed - The training job has completed.

        • Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.

        • Stopping - The training job is stopping.

        • Stopped - The training job has stopped.

        For more detailed information, see SecondaryStatus.

        If the service returns an enum value that is not available in the current SDK version, trainingJobStatus will return TrainingJobStatus.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from trainingJobStatusAsString().

        Returns:
        The status of the training job.

        SageMaker provides the following training job statuses:

        • InProgress - The training is in progress.

        • Completed - The training job has completed.

        • Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.

        • Stopping - The training job is stopping.

        • Stopped - The training job has stopped.

        For more detailed information, see SecondaryStatus.

        See Also:
        TrainingJobStatus

      • secondaryStatus

        public final 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.

        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

        If the service returns an enum value that is not available in the current SDK version, secondaryStatus will return SecondaryStatus.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from secondaryStatusAsString().

        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.

        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

        See Also:
        SecondaryStatus

      • secondaryStatusAsString

        public final String secondaryStatusAsString()

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

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

        InProgress

        • Starting - Starting the training job.

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

        • Training - Training is in progress.

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

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

        Completed

        • Completed - The training job has completed.

        Failed

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

        Stopped

        • MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.

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

        • Stopped - The training job has stopped.

        Stopping

        • Stopping - Stopping the training job.

        Valid values for SecondaryStatus are subject to change.

        We no longer support the following secondary statuses:

        • LaunchingMLInstances

        • PreparingTraining

        • DownloadingTrainingImage

        If the service returns an enum value that is not available in the current SDK version, secondaryStatus will return SecondaryStatus.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from secondaryStatusAsString().

        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.

        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

        See Also:
        SecondaryStatus

      • failureReason

        public final String failureReason()

        If the training job failed, the reason it failed.

        Returns:
        If the training job failed, the reason it failed.

      • hasHyperParameters

        public final boolean hasHyperParameters()
        For responses, this returns true if the service returned a value for the HyperParameters property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • hyperParameters

        public final Map<String,​String> hyperParameters()

        Algorithm-specific parameters.

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasHyperParameters() method.

        Returns:
        Algorithm-specific parameters.

      • algorithmSpecification

        public final AlgorithmSpecification algorithmSpecification()

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

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

      • roleArn

        public final String roleArn()

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

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

      • hasInputDataConfig

        public final boolean hasInputDataConfig()
        For responses, this returns true if the service returned a value for the InputDataConfig property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • inputDataConfig

        public final List<Channel> inputDataConfig()

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

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasInputDataConfig() method.

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

      • outputDataConfig

        public final OutputDataConfig outputDataConfig()

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

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

      • resourceConfig

        public final ResourceConfig resourceConfig()

        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.

      • stoppingCondition

        public final StoppingCondition stoppingCondition()

        Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

        To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

        Returns:
        Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.

        To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost.

      • creationTime

        public final Instant creationTime()

        A timestamp that indicates when the training job was created.

        Returns:
        A timestamp that indicates when the training job was created.

      • trainingStartTime

        public final Instant 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:
        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.

      • trainingEndTime

        public final Instant 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 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 SageMaker detects a job failure.

      • lastModifiedTime

        public final Instant lastModifiedTime()

        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.

      • hasSecondaryStatusTransitions

        public final boolean hasSecondaryStatusTransitions()
        For responses, this returns true if the service returned a value for the SecondaryStatusTransitions property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • secondaryStatusTransitions

        public final List<SecondaryStatusTransition> secondaryStatusTransitions()

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

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasSecondaryStatusTransitions() method.

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

      • hasFinalMetricDataList

        public final boolean hasFinalMetricDataList()
        For responses, this returns true if the service returned a value for the FinalMetricDataList property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • finalMetricDataList

        public final List<MetricData> finalMetricDataList()

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

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasFinalMetricDataList() method.

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

      • enableNetworkIsolation

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

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

      • enableInterContainerTrafficEncryption

        public final 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.

        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.

      • enableManagedSpotTraining

        public final Boolean enableManagedSpotTraining()

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

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

      • checkpointConfig

        public final CheckpointConfig checkpointConfig()
        Returns the value of the CheckpointConfig property for this object.
        Returns:
        The value of the CheckpointConfig property for this object.

      • trainingTimeInSeconds

        public final Integer trainingTimeInSeconds()

        The training time in seconds.

        Returns:
        The training time in seconds.

      • billableTimeInSeconds

        public final Integer billableTimeInSeconds()

        The billable time in seconds. Billable time refers to the absolute wall-clock time.

        Multiply BillableTimeInSeconds by the number of instances (InstanceCount) in your training cluster to get the total compute time SageMaker bills you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount .

        You can calculate the savings from using managed spot training using the formula (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example, if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.

        Returns:
        The billable time in seconds. Billable time refers to the absolute wall-clock time.

        Multiply BillableTimeInSeconds by the number of instances (InstanceCount) in your training cluster to get the total compute time SageMaker bills you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount .

        You can calculate the savings from using managed spot training using the formula (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example, if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.

      • debugHookConfig

        public final DebugHookConfig debugHookConfig()
        Returns the value of the DebugHookConfig property for this object.
        Returns:
        The value of the DebugHookConfig property for this object.

      • experimentConfig

        public final ExperimentConfig experimentConfig()
        Returns the value of the ExperimentConfig property for this object.
        Returns:
        The value of the ExperimentConfig property for this object.

      • hasDebugRuleConfigurations

        public final boolean hasDebugRuleConfigurations()
        For responses, this returns true if the service returned a value for the DebugRuleConfigurations property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • debugRuleConfigurations

        public final List<DebugRuleConfiguration> debugRuleConfigurations()

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

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasDebugRuleConfigurations() method.

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

      • tensorBoardOutputConfig

        public final TensorBoardOutputConfig tensorBoardOutputConfig()
        Returns the value of the TensorBoardOutputConfig property for this object.
        Returns:
        The value of the TensorBoardOutputConfig property for this object.

      • hasDebugRuleEvaluationStatuses

        public final boolean hasDebugRuleEvaluationStatuses()
        For responses, this returns true if the service returned a value for the DebugRuleEvaluationStatuses property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • debugRuleEvaluationStatuses

        public final List<DebugRuleEvaluationStatus> debugRuleEvaluationStatuses()

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

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasDebugRuleEvaluationStatuses() method.

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

      • profilerConfig

        public final ProfilerConfig profilerConfig()
        Returns the value of the ProfilerConfig property for this object.
        Returns:
        The value of the ProfilerConfig property for this object.

      • hasProfilerRuleConfigurations

        public final boolean hasProfilerRuleConfigurations()
        For responses, this returns true if the service returned a value for the ProfilerRuleConfigurations property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • profilerRuleConfigurations

        public final List<ProfilerRuleConfiguration> profilerRuleConfigurations()

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

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasProfilerRuleConfigurations() method.

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

      • hasProfilerRuleEvaluationStatuses

        public final boolean hasProfilerRuleEvaluationStatuses()
        For responses, this returns true if the service returned a value for the ProfilerRuleEvaluationStatuses property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • profilerRuleEvaluationStatuses

        public final List<ProfilerRuleEvaluationStatus> profilerRuleEvaluationStatuses()

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

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasProfilerRuleEvaluationStatuses() method.

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

      • retryStrategy

        public final RetryStrategy retryStrategy()

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

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

      • hasEnvironment

        public final boolean hasEnvironment()
        For responses, this returns true if the service returned a value for the Environment property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • environment

        public final Map<String,​String> environment()

        The environment variables to set in the Docker container.

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasEnvironment() method.

        Returns:
        The environment variables to set in the Docker container.

      • warmPoolStatus

        public final WarmPoolStatus warmPoolStatus()

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

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

      • infraCheckConfig

        public final InfraCheckConfig infraCheckConfig()

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

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

      • toString

        public final 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