libZSservicesZSamazonka-sagemakerZSamazonka-sagemaker
Copyright(c) 2013-2021 Brendan Hay
LicenseMozilla Public License, v. 2.0.
MaintainerBrendan Hay <brendan.g.hay+amazonka@gmail.com>
Stabilityauto-generated
Portabilitynon-portable (GHC extensions)
Safe HaskellNone

Amazonka.SageMaker.DescribeTrainingJob

Description

Returns information about a training job.

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

Synopsis

Creating a Request

data DescribeTrainingJob Source #

See: newDescribeTrainingJob smart constructor.

Constructors

DescribeTrainingJob' 

Fields

Instances

Instances details
Eq DescribeTrainingJob Source # 
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Read DescribeTrainingJob Source # 
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Show DescribeTrainingJob Source # 
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Generic DescribeTrainingJob Source # 
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Associated Types

type Rep DescribeTrainingJob :: Type -> Type #

NFData DescribeTrainingJob Source # 
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Methods

rnf :: DescribeTrainingJob -> () #

Hashable DescribeTrainingJob Source # 
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ToJSON DescribeTrainingJob Source # 
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AWSRequest DescribeTrainingJob Source # 
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Associated Types

type AWSResponse DescribeTrainingJob #

ToHeaders DescribeTrainingJob Source # 
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ToPath DescribeTrainingJob Source # 
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ToQuery DescribeTrainingJob Source # 
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type Rep DescribeTrainingJob Source # 
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type Rep DescribeTrainingJob = D1 ('MetaData "DescribeTrainingJob" "Amazonka.SageMaker.DescribeTrainingJob" "libZSservicesZSamazonka-sagemakerZSamazonka-sagemaker" 'False) (C1 ('MetaCons "DescribeTrainingJob'" 'PrefixI 'True) (S1 ('MetaSel ('Just "trainingJobName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))
type AWSResponse DescribeTrainingJob Source # 
Instance details

Defined in Amazonka.SageMaker.DescribeTrainingJob

newDescribeTrainingJob Source #

Create a value of DescribeTrainingJob with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:trainingJobName:DescribeTrainingJob', describeTrainingJob_trainingJobName - The name of the training job.

Request Lenses

Destructuring the Response

data DescribeTrainingJobResponse Source #

See: newDescribeTrainingJobResponse smart constructor.

Constructors

DescribeTrainingJobResponse' 

Fields

  • labelingJobArn :: Maybe Text

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

  • failureReason :: Maybe Text

    If the training job failed, the reason it failed.

  • secondaryStatusTransitions :: Maybe [SecondaryStatusTransition]

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

  • trainingEndTime :: Maybe POSIX

    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.

  • environment :: Maybe (HashMap Text Text)

    The environment variables to set in the Docker container.

  • billableTimeInSeconds :: Maybe Natural

    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 will bill you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount .

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

  • debugHookConfig :: Maybe DebugHookConfig
     
  • checkpointConfig :: Maybe CheckpointConfig
     
  • retryStrategy :: Maybe RetryStrategy

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

  • debugRuleEvaluationStatuses :: Maybe [DebugRuleEvaluationStatus]

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

  • profilerConfig :: Maybe ProfilerConfig
     
  • profilerRuleEvaluationStatuses :: Maybe [ProfilerRuleEvaluationStatus]

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

  • enableNetworkIsolation :: Maybe Bool

    If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

  • experimentConfig :: Maybe ExperimentConfig
     
  • lastModifiedTime :: Maybe POSIX

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

  • debugRuleConfigurations :: Maybe [DebugRuleConfiguration]

    Configuration information for Debugger rules for debugging output tensors.

  • enableManagedSpotTraining :: Maybe Bool

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

  • autoMLJobArn :: Maybe Text

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

  • hyperParameters :: Maybe (HashMap Text Text)

    Algorithm-specific parameters.

  • inputDataConfig :: Maybe (NonEmpty Channel)

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

  • profilerRuleConfigurations :: Maybe [ProfilerRuleConfiguration]

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

  • vpcConfig :: Maybe 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.

  • finalMetricDataList :: Maybe [MetricData]

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

  • profilingStatus :: Maybe ProfilingStatus

    Profiling status of a training job.

  • outputDataConfig :: Maybe OutputDataConfig

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

  • trainingStartTime :: Maybe POSIX

    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.

  • tuningJobArn :: Maybe Text

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

  • enableInterContainerTrafficEncryption :: Maybe Bool

    To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithms in distributed training.

  • tensorBoardOutputConfig :: Maybe TensorBoardOutputConfig
     
  • trainingTimeInSeconds :: Maybe Natural

    The training time in seconds.

  • roleArn :: Maybe Text

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

  • httpStatus :: Int

    The response's http status code.

  • trainingJobName :: Text

    Name of the model training job.

  • trainingJobArn :: Text

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

  • modelArtifacts :: ModelArtifacts

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

  • 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.

  • 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.
    • 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
  • algorithmSpecification :: AlgorithmSpecification

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

  • resourceConfig :: ResourceConfig

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

  • 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, 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.

  • creationTime :: POSIX

    A timestamp that indicates when the training job was created.

Instances

Instances details
Eq DescribeTrainingJobResponse Source # 
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Defined in Amazonka.SageMaker.DescribeTrainingJob

Read DescribeTrainingJobResponse Source # 
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Show DescribeTrainingJobResponse Source # 
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Generic DescribeTrainingJobResponse Source # 
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Associated Types

type Rep DescribeTrainingJobResponse :: Type -> Type #

NFData DescribeTrainingJobResponse Source # 
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type Rep DescribeTrainingJobResponse Source # 
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type Rep DescribeTrainingJobResponse = D1 ('MetaData "DescribeTrainingJobResponse" "Amazonka.SageMaker.DescribeTrainingJob" "libZSservicesZSamazonka-sagemakerZSamazonka-sagemaker" 'False) (C1 ('MetaCons "DescribeTrainingJobResponse'" 'PrefixI 'True) (((((S1 ('MetaSel ('Just "labelingJobArn") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "failureReason") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :*: (S1 ('MetaSel ('Just "secondaryStatusTransitions") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [SecondaryStatusTransition])) :*: (S1 ('MetaSel ('Just "trainingEndTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :*: S1 ('MetaSel ('Just "environment") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe (HashMap Text Text)))))) :*: ((S1 ('MetaSel ('Just "billableTimeInSeconds") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Natural)) :*: S1 ('MetaSel ('Just "debugHookConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe DebugHookConfig))) :*: (S1 ('MetaSel ('Just "checkpointConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe CheckpointConfig)) :*: (S1 ('MetaSel ('Just "retryStrategy") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe RetryStrategy)) :*: S1 ('MetaSel ('Just "debugRuleEvaluationStatuses") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [DebugRuleEvaluationStatus])))))) :*: (((S1 ('MetaSel ('Just "profilerConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe ProfilerConfig)) :*: S1 ('MetaSel ('Just "profilerRuleEvaluationStatuses") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [ProfilerRuleEvaluationStatus]))) :*: (S1 ('MetaSel ('Just "enableNetworkIsolation") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool)) :*: (S1 ('MetaSel ('Just "experimentConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe ExperimentConfig)) :*: S1 ('MetaSel ('Just "lastModifiedTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX))))) :*: ((S1 ('MetaSel ('Just "debugRuleConfigurations") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [DebugRuleConfiguration])) :*: S1 ('MetaSel ('Just "enableManagedSpotTraining") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool))) :*: (S1 ('MetaSel ('Just "autoMLJobArn") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: (S1 ('MetaSel ('Just "hyperParameters") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe (HashMap Text Text))) :*: S1 ('MetaSel ('Just "inputDataConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe (NonEmpty Channel)))))))) :*: ((((S1 ('MetaSel ('Just "profilerRuleConfigurations") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [ProfilerRuleConfiguration])) :*: S1 ('MetaSel ('Just "vpcConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe VpcConfig))) :*: (S1 ('MetaSel ('Just "finalMetricDataList") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [MetricData])) :*: (S1 ('MetaSel ('Just "profilingStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe ProfilingStatus)) :*: S1 ('MetaSel ('Just "outputDataConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe OutputDataConfig))))) :*: ((S1 ('MetaSel ('Just "trainingStartTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :*: S1 ('MetaSel ('Just "tuningJobArn") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :*: (S1 ('MetaSel ('Just "enableInterContainerTrafficEncryption") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool)) :*: (S1 ('MetaSel ('Just "tensorBoardOutputConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe TensorBoardOutputConfig)) :*: S1 ('MetaSel ('Just "trainingTimeInSeconds") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Natural)))))) :*: (((S1 ('MetaSel ('Just "roleArn") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)) :*: (S1 ('MetaSel ('Just "trainingJobName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :*: (S1 ('MetaSel ('Just "trainingJobArn") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :*: S1 ('MetaSel ('Just "modelArtifacts") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 ModelArtifacts)))) :*: ((S1 ('MetaSel ('Just "trainingJobStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 TrainingJobStatus) :*: (S1 ('MetaSel ('Just "secondaryStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 SecondaryStatus) :*: S1 ('MetaSel ('Just "algorithmSpecification") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 AlgorithmSpecification))) :*: (S1 ('MetaSel ('Just "resourceConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 ResourceConfig) :*: (S1 ('MetaSel ('Just "stoppingCondition") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 StoppingCondition) :*: S1 ('MetaSel ('Just "creationTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 POSIX))))))))

newDescribeTrainingJobResponse Source #

Create a value of DescribeTrainingJobResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:labelingJobArn:DescribeTrainingJobResponse', describeTrainingJobResponse_labelingJobArn - The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.

$sel:failureReason:DescribeTrainingJobResponse', describeTrainingJobResponse_failureReason - If the training job failed, the reason it failed.

$sel:secondaryStatusTransitions:DescribeTrainingJobResponse', describeTrainingJobResponse_secondaryStatusTransitions - A history of all of the secondary statuses that the training job has transitioned through.

$sel:trainingEndTime:DescribeTrainingJobResponse', describeTrainingJobResponse_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.

$sel:environment:DescribeTrainingJobResponse', describeTrainingJobResponse_environment - The environment variables to set in the Docker container.

$sel:billableTimeInSeconds:DescribeTrainingJobResponse', describeTrainingJobResponse_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 will bill you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount .

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

$sel:debugHookConfig:DescribeTrainingJobResponse', describeTrainingJobResponse_debugHookConfig - Undocumented member.

$sel:checkpointConfig:DescribeTrainingJobResponse', describeTrainingJobResponse_checkpointConfig - Undocumented member.

$sel:retryStrategy:DescribeTrainingJobResponse', describeTrainingJobResponse_retryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.

$sel:debugRuleEvaluationStatuses:DescribeTrainingJobResponse', describeTrainingJobResponse_debugRuleEvaluationStatuses - Evaluation status of Debugger rules for debugging on a training job.

$sel:profilerConfig:DescribeTrainingJobResponse', describeTrainingJobResponse_profilerConfig - Undocumented member.

$sel:profilerRuleEvaluationStatuses:DescribeTrainingJobResponse', describeTrainingJobResponse_profilerRuleEvaluationStatuses - Evaluation status of Debugger rules for profiling on a training job.

$sel:enableNetworkIsolation:DescribeTrainingJobResponse', describeTrainingJobResponse_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.

$sel:experimentConfig:DescribeTrainingJobResponse', describeTrainingJobResponse_experimentConfig - Undocumented member.

$sel:lastModifiedTime:DescribeTrainingJobResponse', describeTrainingJobResponse_lastModifiedTime - A timestamp that indicates when the status of the training job was last modified.

$sel:debugRuleConfigurations:DescribeTrainingJobResponse', describeTrainingJobResponse_debugRuleConfigurations - Configuration information for Debugger rules for debugging output tensors.

$sel:enableManagedSpotTraining:DescribeTrainingJobResponse', describeTrainingJobResponse_enableManagedSpotTraining - A Boolean indicating whether managed spot training is enabled (True) or not (False).

$sel:autoMLJobArn:DescribeTrainingJobResponse', describeTrainingJobResponse_autoMLJobArn - The Amazon Resource Name (ARN) of an AutoML job.

$sel:hyperParameters:DescribeTrainingJobResponse', describeTrainingJobResponse_hyperParameters - Algorithm-specific parameters.

$sel:inputDataConfig:DescribeTrainingJobResponse', describeTrainingJobResponse_inputDataConfig - An array of Channel objects that describes each data input channel.

$sel:profilerRuleConfigurations:DescribeTrainingJobResponse', describeTrainingJobResponse_profilerRuleConfigurations - Configuration information for Debugger rules for profiling system and framework metrics.

$sel:vpcConfig:DescribeTrainingJobResponse', describeTrainingJobResponse_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.

$sel:finalMetricDataList:DescribeTrainingJobResponse', describeTrainingJobResponse_finalMetricDataList - A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.

$sel:profilingStatus:DescribeTrainingJobResponse', describeTrainingJobResponse_profilingStatus - Profiling status of a training job.

$sel:outputDataConfig:DescribeTrainingJobResponse', describeTrainingJobResponse_outputDataConfig - The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.

$sel:trainingStartTime:DescribeTrainingJobResponse', describeTrainingJobResponse_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.

$sel:tuningJobArn:DescribeTrainingJobResponse', describeTrainingJobResponse_tuningJobArn - The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.

$sel:enableInterContainerTrafficEncryption:DescribeTrainingJobResponse', describeTrainingJobResponse_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.

$sel:tensorBoardOutputConfig:DescribeTrainingJobResponse', describeTrainingJobResponse_tensorBoardOutputConfig - Undocumented member.

$sel:trainingTimeInSeconds:DescribeTrainingJobResponse', describeTrainingJobResponse_trainingTimeInSeconds - The training time in seconds.

$sel:roleArn:DescribeTrainingJobResponse', describeTrainingJobResponse_roleArn - The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.

$sel:httpStatus:DescribeTrainingJobResponse', describeTrainingJobResponse_httpStatus - The response's http status code.

$sel:trainingJobName:DescribeTrainingJob', describeTrainingJobResponse_trainingJobName - Name of the model training job.

$sel:trainingJobArn:DescribeTrainingJobResponse', describeTrainingJobResponse_trainingJobArn - The Amazon Resource Name (ARN) of the training job.

$sel:modelArtifacts:DescribeTrainingJobResponse', describeTrainingJobResponse_modelArtifacts - Information about the Amazon S3 location that is configured for storing model artifacts.

$sel:trainingJobStatus:DescribeTrainingJobResponse', describeTrainingJobResponse_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.

$sel:secondaryStatus:DescribeTrainingJobResponse', describeTrainingJobResponse_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.
  • 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

$sel:algorithmSpecification:DescribeTrainingJobResponse', describeTrainingJobResponse_algorithmSpecification - Information about the algorithm used for training, and algorithm metadata.

$sel:resourceConfig:DescribeTrainingJobResponse', describeTrainingJobResponse_resourceConfig - Resources, including ML compute instances and ML storage volumes, that are configured for model training.

$sel:stoppingCondition:DescribeTrainingJobResponse', describeTrainingJobResponse_stoppingCondition - Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.

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

$sel:creationTime:DescribeTrainingJobResponse', describeTrainingJobResponse_creationTime - A timestamp that indicates when the training job was created.

Response Lenses

describeTrainingJobResponse_labelingJobArn :: Lens' DescribeTrainingJobResponse (Maybe Text) Source #

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

describeTrainingJobResponse_failureReason :: Lens' DescribeTrainingJobResponse (Maybe Text) Source #

If the training job failed, the reason it failed.

describeTrainingJobResponse_secondaryStatusTransitions :: Lens' DescribeTrainingJobResponse (Maybe [SecondaryStatusTransition]) Source #

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

describeTrainingJobResponse_trainingEndTime :: Lens' DescribeTrainingJobResponse (Maybe UTCTime) Source #

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.

describeTrainingJobResponse_environment :: Lens' DescribeTrainingJobResponse (Maybe (HashMap Text Text)) Source #

The environment variables to set in the Docker container.

describeTrainingJobResponse_billableTimeInSeconds :: Lens' DescribeTrainingJobResponse (Maybe Natural) Source #

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 will bill you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount .

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

describeTrainingJobResponse_retryStrategy :: Lens' DescribeTrainingJobResponse (Maybe RetryStrategy) Source #

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

describeTrainingJobResponse_enableNetworkIsolation :: Lens' DescribeTrainingJobResponse (Maybe Bool) Source #

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.

describeTrainingJobResponse_lastModifiedTime :: Lens' DescribeTrainingJobResponse (Maybe UTCTime) Source #

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

describeTrainingJobResponse_debugRuleConfigurations :: Lens' DescribeTrainingJobResponse (Maybe [DebugRuleConfiguration]) Source #

Configuration information for Debugger rules for debugging output tensors.

describeTrainingJobResponse_enableManagedSpotTraining :: Lens' DescribeTrainingJobResponse (Maybe Bool) Source #

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

describeTrainingJobResponse_autoMLJobArn :: Lens' DescribeTrainingJobResponse (Maybe Text) Source #

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

describeTrainingJobResponse_inputDataConfig :: Lens' DescribeTrainingJobResponse (Maybe (NonEmpty Channel)) Source #

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

describeTrainingJobResponse_profilerRuleConfigurations :: Lens' DescribeTrainingJobResponse (Maybe [ProfilerRuleConfiguration]) Source #

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

describeTrainingJobResponse_vpcConfig :: Lens' DescribeTrainingJobResponse (Maybe VpcConfig) Source #

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.

describeTrainingJobResponse_finalMetricDataList :: Lens' DescribeTrainingJobResponse (Maybe [MetricData]) Source #

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

describeTrainingJobResponse_outputDataConfig :: Lens' DescribeTrainingJobResponse (Maybe OutputDataConfig) Source #

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

describeTrainingJobResponse_trainingStartTime :: Lens' DescribeTrainingJobResponse (Maybe UTCTime) Source #

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.

describeTrainingJobResponse_tuningJobArn :: Lens' DescribeTrainingJobResponse (Maybe Text) Source #

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

describeTrainingJobResponse_enableInterContainerTrafficEncryption :: Lens' DescribeTrainingJobResponse (Maybe Bool) Source #

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.

describeTrainingJobResponse_roleArn :: Lens' DescribeTrainingJobResponse (Maybe Text) Source #

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

describeTrainingJobResponse_trainingJobArn :: Lens' DescribeTrainingJobResponse Text Source #

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

describeTrainingJobResponse_modelArtifacts :: Lens' DescribeTrainingJobResponse ModelArtifacts Source #

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

describeTrainingJobResponse_trainingJobStatus :: Lens' DescribeTrainingJobResponse TrainingJobStatus Source #

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.

describeTrainingJobResponse_secondaryStatus :: Lens' DescribeTrainingJobResponse SecondaryStatus Source #

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

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

InProgress
- Starting - Starting the training job.
  • Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
  • Training - Training is in progress.
  • Interrupted - The job stopped because the managed spot training instances were interrupted.
  • Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Completed
- Completed - The training job has completed.
Failed
- Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.
Stopped
- MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
  • MaxWaitTimeExceeded - The job stopped because it exceeded the maximum allowed wait time.
  • Stopped - The training job has stopped.
Stopping
- Stopping - Stopping the training job.

Valid values for SecondaryStatus are subject to change.

We no longer support the following secondary statuses:

  • LaunchingMLInstances
  • PreparingTraining
  • DownloadingTrainingImage

describeTrainingJobResponse_algorithmSpecification :: Lens' DescribeTrainingJobResponse AlgorithmSpecification Source #

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

describeTrainingJobResponse_resourceConfig :: Lens' DescribeTrainingJobResponse ResourceConfig Source #

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

describeTrainingJobResponse_stoppingCondition :: Lens' DescribeTrainingJobResponse StoppingCondition Source #

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

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

describeTrainingJobResponse_creationTime :: Lens' DescribeTrainingJobResponse UTCTime Source #

A timestamp that indicates when the training job was created.