Amazonka.SageMaker.DescribeTrainingJob

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.

Creating a Request

data DescribeTrainingJob

See: newDescribeTrainingJob smart constructor.

Constructors

DescribeTrainingJob'

Fields

• trainingJobName :: Text

  The name of the training job.

Instances

ToJSON DescribeTrainingJob

Defined in Amazonka.SageMaker.DescribeTrainingJob

Methods

toJSON :: DescribeTrainingJob -> Value

toEncoding :: DescribeTrainingJob -> Encoding

toJSONList :: [DescribeTrainingJob] -> Value

toEncodingList :: [DescribeTrainingJob] -> Encoding

ToHeaders DescribeTrainingJob

Defined in Amazonka.SageMaker.DescribeTrainingJob

Methods

toHeaders :: DescribeTrainingJob -> [Header]

ToPath DescribeTrainingJob

Defined in Amazonka.SageMaker.DescribeTrainingJob

Methods

toPath :: DescribeTrainingJob -> ByteString

ToQuery DescribeTrainingJob

Defined in Amazonka.SageMaker.DescribeTrainingJob

Methods

toQuery :: DescribeTrainingJob -> QueryString

AWSRequest DescribeTrainingJob

Defined in Amazonka.SageMaker.DescribeTrainingJob

Associated Types

type AWSResponse DescribeTrainingJob

Methods

request :: (Service -> Service) -> DescribeTrainingJob -> Request DescribeTrainingJob

response :: MonadResource m => (ByteStringLazy -> IO ByteStringLazy) -> Service -> Proxy DescribeTrainingJob -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse DescribeTrainingJob)))

Generic DescribeTrainingJob

Defined in Amazonka.SageMaker.DescribeTrainingJob

Associated Types

type Rep DescribeTrainingJob :: Type -> Type

Methods

from :: DescribeTrainingJob -> Rep DescribeTrainingJob x

to :: Rep DescribeTrainingJob x -> DescribeTrainingJob

Read DescribeTrainingJob

Defined in Amazonka.SageMaker.DescribeTrainingJob

Methods

readsPrec :: Int -> ReadS DescribeTrainingJob

readList :: ReadS [DescribeTrainingJob]

readPrec :: ReadPrec DescribeTrainingJob

readListPrec :: ReadPrec [DescribeTrainingJob]

Show DescribeTrainingJob

Defined in Amazonka.SageMaker.DescribeTrainingJob

Methods

showsPrec :: Int -> DescribeTrainingJob -> ShowS

show :: DescribeTrainingJob -> String

showList :: [DescribeTrainingJob] -> ShowS

NFData DescribeTrainingJob

Defined in Amazonka.SageMaker.DescribeTrainingJob

Methods

rnf :: DescribeTrainingJob -> ()

Eq DescribeTrainingJob

Defined in Amazonka.SageMaker.DescribeTrainingJob

Methods

(==) :: DescribeTrainingJob -> DescribeTrainingJob -> Bool

(/=) :: DescribeTrainingJob -> DescribeTrainingJob -> Bool

Hashable DescribeTrainingJob

Defined in Amazonka.SageMaker.DescribeTrainingJob

Methods

hashWithSalt :: Int -> DescribeTrainingJob -> Int

hash :: DescribeTrainingJob -> Int

type AWSResponse DescribeTrainingJob

Defined in Amazonka.SageMaker.DescribeTrainingJob

type AWSResponse DescribeTrainingJob = DescribeTrainingJobResponse

type Rep DescribeTrainingJob

Defined in Amazonka.SageMaker.DescribeTrainingJob

type Rep DescribeTrainingJob = D1 ('MetaData "DescribeTrainingJob" "Amazonka.SageMaker.DescribeTrainingJob" "amazonka-sagemaker-2.0-9SyrKZ4KqhsL1qX9u3ILA3" 'False) (C1 ('MetaCons "DescribeTrainingJob'" 'PrefixI 'True) (S1 ('MetaSel ('Just "trainingJobName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))

newDescribeTrainingJob

Arguments

:: Text

DescribeTrainingJob

-> DescribeTrainingJob

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:

DescribeTrainingJob, describeTrainingJob_trainingJobName - The name of the training job.

Request Lenses

describeTrainingJob_trainingJobName :: Lens' DescribeTrainingJob Text

The name of the training job.

Destructuring the Response

data DescribeTrainingJobResponse

See: newDescribeTrainingJobResponse smart constructor.

Constructors

DescribeTrainingJobResponse'

Fields

• autoMLJobArn :: Maybe Text

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

• 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 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%.

• checkpointConfig :: Maybe CheckpointConfig
• debugHookConfig :: Maybe DebugHookConfig
• debugRuleConfigurations :: Maybe [DebugRuleConfiguration]

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

• debugRuleEvaluationStatuses :: Maybe [DebugRuleEvaluationStatus]

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

• enableManagedSpotTraining :: Maybe Bool

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

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

• environment :: Maybe (HashMap Text Text)

  The environment variables to set in the Docker container.

• experimentConfig :: Maybe ExperimentConfig
• failureReason :: Maybe Text

  If the training job failed, the reason it failed.

• 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.

• hyperParameters :: Maybe (HashMap Text Text)

  Algorithm-specific parameters.

• inputDataConfig :: Maybe (NonEmpty Channel)

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

• labelingJobArn :: Maybe Text

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

• lastModifiedTime :: Maybe POSIX

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

• outputDataConfig :: Maybe OutputDataConfig

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

• profilerConfig :: Maybe ProfilerConfig
• profilerRuleConfigurations :: Maybe [ProfilerRuleConfiguration]

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

• profilerRuleEvaluationStatuses :: Maybe [ProfilerRuleEvaluationStatus]

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

• profilingStatus :: Maybe ProfilingStatus

  Profiling status of a training job.

• retryStrategy :: Maybe RetryStrategy

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

• roleArn :: Maybe Text

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

• secondaryStatusTransitions :: Maybe [SecondaryStatusTransition]

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

• tensorBoardOutputConfig :: Maybe TensorBoardOutputConfig
• 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 SageMaker detects a job failure.

• 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.

• trainingTimeInSeconds :: Maybe Natural

  The training time in seconds.

• 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.

• 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.

• warmPoolStatus :: Maybe WarmPoolStatus

  The status of the warm pool associated with 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.

  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.

  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, 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 :: POSIX

  A timestamp that indicates when the training job was created.

Instances

Generic DescribeTrainingJobResponse

Defined in Amazonka.SageMaker.DescribeTrainingJob

Associated Types

type Rep DescribeTrainingJobResponse :: Type -> Type

Methods

from :: DescribeTrainingJobResponse -> Rep DescribeTrainingJobResponse x

to :: Rep DescribeTrainingJobResponse x -> DescribeTrainingJobResponse

Read DescribeTrainingJobResponse

Defined in Amazonka.SageMaker.DescribeTrainingJob

Methods

readsPrec :: Int -> ReadS DescribeTrainingJobResponse

readList :: ReadS [DescribeTrainingJobResponse]

readPrec :: ReadPrec DescribeTrainingJobResponse

readListPrec :: ReadPrec [DescribeTrainingJobResponse]

Show DescribeTrainingJobResponse

Defined in Amazonka.SageMaker.DescribeTrainingJob

Methods

showsPrec :: Int -> DescribeTrainingJobResponse -> ShowS

show :: DescribeTrainingJobResponse -> String

showList :: [DescribeTrainingJobResponse] -> ShowS

NFData DescribeTrainingJobResponse

Defined in Amazonka.SageMaker.DescribeTrainingJob

Methods

rnf :: DescribeTrainingJobResponse -> ()

Eq DescribeTrainingJobResponse

Defined in Amazonka.SageMaker.DescribeTrainingJob

Methods

(==) :: DescribeTrainingJobResponse -> DescribeTrainingJobResponse -> Bool

(/=) :: DescribeTrainingJobResponse -> DescribeTrainingJobResponse -> Bool

type Rep DescribeTrainingJobResponse

Defined in Amazonka.SageMaker.DescribeTrainingJob

type Rep DescribeTrainingJobResponse = D1 ('MetaData "DescribeTrainingJobResponse" "Amazonka.SageMaker.DescribeTrainingJob" "amazonka-sagemaker-2.0-9SyrKZ4KqhsL1qX9u3ILA3" 'False) (C1 ('MetaCons "DescribeTrainingJobResponse'" 'PrefixI 'True) (((((S1 ('MetaSel ('Just "autoMLJobArn") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "billableTimeInSeconds") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Natural))) :*: (S1 ('MetaSel ('Just "checkpointConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe CheckpointConfig)) :*: (S1 ('MetaSel ('Just "debugHookConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe DebugHookConfig)) :*: S1 ('MetaSel ('Just "debugRuleConfigurations") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [DebugRuleConfiguration]))))) :*: ((S1 ('MetaSel ('Just "debugRuleEvaluationStatuses") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [DebugRuleEvaluationStatus])) :*: S1 ('MetaSel ('Just "enableInterContainerTrafficEncryption") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool))) :*: (S1 ('MetaSel ('Just "enableManagedSpotTraining") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool)) :*: (S1 ('MetaSel ('Just "enableNetworkIsolation") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool)) :*: S1 ('MetaSel ('Just "environment") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe (HashMap Text Text))))))) :*: (((S1 ('MetaSel ('Just "experimentConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe ExperimentConfig)) :*: S1 ('MetaSel ('Just "failureReason") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :*: (S1 ('MetaSel ('Just "finalMetricDataList") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [MetricData])) :*: (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 "labelingJobArn") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: (S1 ('MetaSel ('Just "lastModifiedTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :*: S1 ('MetaSel ('Just "outputDataConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe OutputDataConfig)))) :*: (S1 ('MetaSel ('Just "profilerConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe ProfilerConfig)) :*: (S1 ('MetaSel ('Just "profilerRuleConfigurations") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [ProfilerRuleConfiguration])) :*: S1 ('MetaSel ('Just "profilerRuleEvaluationStatuses") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [ProfilerRuleEvaluationStatus]))))))) :*: ((((S1 ('MetaSel ('Just "profilingStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe ProfilingStatus)) :*: S1 ('MetaSel ('Just "retryStrategy") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe RetryStrategy))) :*: (S1 ('MetaSel ('Just "roleArn") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: (S1 ('MetaSel ('Just "secondaryStatusTransitions") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [SecondaryStatusTransition])) :*: S1 ('MetaSel ('Just "tensorBoardOutputConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe TensorBoardOutputConfig))))) :*: ((S1 ('MetaSel ('Just "trainingEndTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :*: S1 ('MetaSel ('Just "trainingStartTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX))) :*: (S1 ('MetaSel ('Just "trainingTimeInSeconds") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Natural)) :*: (S1 ('MetaSel ('Just "tuningJobArn") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "vpcConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe VpcConfig)))))) :*: (((S1 ('MetaSel ('Just "warmPoolStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe WarmPoolStatus)) :*: 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

Arguments

:: Int

$sel:httpStatus:DescribeTrainingJobResponse'

-> Text

DescribeTrainingJob

-> Text

DescribeTrainingJobResponse

-> ModelArtifacts

DescribeTrainingJobResponse

-> TrainingJobStatus

DescribeTrainingJobResponse

-> SecondaryStatus

DescribeTrainingJobResponse

-> AlgorithmSpecification

DescribeTrainingJobResponse

-> ResourceConfig

DescribeTrainingJobResponse

-> StoppingCondition

DescribeTrainingJobResponse

-> UTCTime

DescribeTrainingJobResponse

-> DescribeTrainingJobResponse

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:

DescribeTrainingJobResponse, describeTrainingJobResponse_autoMLJobArn - The Amazon Resource Name (ARN) of an AutoML job.

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 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%.

DescribeTrainingJobResponse, describeTrainingJobResponse_checkpointConfig - Undocumented member.

DescribeTrainingJobResponse, describeTrainingJobResponse_debugHookConfig - Undocumented member.

DescribeTrainingJobResponse, describeTrainingJobResponse_debugRuleConfigurations - Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.

DescribeTrainingJobResponse, describeTrainingJobResponse_debugRuleEvaluationStatuses - Evaluation status of Amazon SageMaker Debugger rules for debugging on a training job.

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.

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

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

DescribeTrainingJobResponse, describeTrainingJobResponse_environment - The environment variables to set in the Docker container.

DescribeTrainingJobResponse, describeTrainingJobResponse_experimentConfig - Undocumented member.

DescribeTrainingJobResponse, describeTrainingJobResponse_failureReason - If the training job failed, the reason it failed.

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.

DescribeTrainingJobResponse, describeTrainingJobResponse_hyperParameters - Algorithm-specific parameters.

DescribeTrainingJobResponse, describeTrainingJobResponse_inputDataConfig - An array of Channel objects that describes each data input channel.

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

DescribeTrainingJobResponse, describeTrainingJobResponse_lastModifiedTime - A timestamp that indicates when the status of the training job was last modified.

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

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

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

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

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

DescribeTrainingJobResponse, describeTrainingJobResponse_retryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.

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

DescribeTrainingJobResponse, describeTrainingJobResponse_secondaryStatusTransitions - A history of all of the secondary statuses that the training job has transitioned through.

DescribeTrainingJobResponse, describeTrainingJobResponse_tensorBoardOutputConfig - Undocumented member.

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 SageMaker detects a job failure.

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.

DescribeTrainingJobResponse, describeTrainingJobResponse_trainingTimeInSeconds - The training time in seconds.

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.

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.

DescribeTrainingJobResponse, describeTrainingJobResponse_warmPoolStatus - The status of the warm pool associated with the training job.

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

DescribeTrainingJob, describeTrainingJobResponse_trainingJobName - Name of the model training job.

DescribeTrainingJobResponse, describeTrainingJobResponse_trainingJobArn - The Amazon Resource Name (ARN) of the training job.

DescribeTrainingJobResponse, describeTrainingJobResponse_modelArtifacts - Information about the Amazon S3 location that is configured for storing model artifacts.

DescribeTrainingJobResponse, describeTrainingJobResponse_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.

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.

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

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

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, 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.

DescribeTrainingJobResponse, describeTrainingJobResponse_creationTime - A timestamp that indicates when the training job was created.

Response Lenses

describeTrainingJobResponse_autoMLJobArn :: Lens' DescribeTrainingJobResponse (Maybe Text)

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

describeTrainingJobResponse_billableTimeInSeconds :: Lens' DescribeTrainingJobResponse (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 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%.

describeTrainingJobResponse_checkpointConfig :: Lens' DescribeTrainingJobResponse (Maybe CheckpointConfig)

Undocumented member.

describeTrainingJobResponse_debugHookConfig :: Lens' DescribeTrainingJobResponse (Maybe DebugHookConfig)

Undocumented member.

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

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

describeTrainingJobResponse_debugRuleEvaluationStatuses :: Lens' DescribeTrainingJobResponse (Maybe [DebugRuleEvaluationStatus])

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

describeTrainingJobResponse_enableInterContainerTrafficEncryption :: Lens' DescribeTrainingJobResponse (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.

describeTrainingJobResponse_enableManagedSpotTraining :: Lens' DescribeTrainingJobResponse (Maybe Bool)

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

describeTrainingJobResponse_enableNetworkIsolation :: Lens' DescribeTrainingJobResponse (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, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

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

The environment variables to set in the Docker container.

describeTrainingJobResponse_experimentConfig :: Lens' DescribeTrainingJobResponse (Maybe ExperimentConfig)

Undocumented member.

describeTrainingJobResponse_failureReason :: Lens' DescribeTrainingJobResponse (Maybe Text)

If the training job failed, the reason it failed.

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

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

describeTrainingJobResponse_hyperParameters :: Lens' DescribeTrainingJobResponse (Maybe (HashMap Text Text))

Algorithm-specific parameters.

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

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

describeTrainingJobResponse_labelingJobArn :: Lens' DescribeTrainingJobResponse (Maybe Text)

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

describeTrainingJobResponse_lastModifiedTime :: Lens' DescribeTrainingJobResponse (Maybe UTCTime)

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

describeTrainingJobResponse_outputDataConfig :: Lens' DescribeTrainingJobResponse (Maybe OutputDataConfig)

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

describeTrainingJobResponse_profilerConfig :: Lens' DescribeTrainingJobResponse (Maybe ProfilerConfig)

Undocumented member.

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

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

describeTrainingJobResponse_profilerRuleEvaluationStatuses :: Lens' DescribeTrainingJobResponse (Maybe [ProfilerRuleEvaluationStatus])

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

describeTrainingJobResponse_profilingStatus :: Lens' DescribeTrainingJobResponse (Maybe ProfilingStatus)

Profiling status of a training job.

describeTrainingJobResponse_retryStrategy :: Lens' DescribeTrainingJobResponse (Maybe RetryStrategy)

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

describeTrainingJobResponse_roleArn :: Lens' DescribeTrainingJobResponse (Maybe Text)

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

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

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

describeTrainingJobResponse_tensorBoardOutputConfig :: Lens' DescribeTrainingJobResponse (Maybe TensorBoardOutputConfig)

Undocumented member.

describeTrainingJobResponse_trainingEndTime :: Lens' DescribeTrainingJobResponse (Maybe UTCTime)

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.

describeTrainingJobResponse_trainingStartTime :: Lens' DescribeTrainingJobResponse (Maybe UTCTime)

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_trainingTimeInSeconds :: Lens' DescribeTrainingJobResponse (Maybe Natural)

The training time in seconds.

describeTrainingJobResponse_tuningJobArn :: Lens' DescribeTrainingJobResponse (Maybe Text)

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

describeTrainingJobResponse_vpcConfig :: Lens' DescribeTrainingJobResponse (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.

describeTrainingJobResponse_warmPoolStatus :: Lens' DescribeTrainingJobResponse (Maybe WarmPoolStatus)

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

describeTrainingJobResponse_httpStatus :: Lens' DescribeTrainingJobResponse Int

The response's http status code.

describeTrainingJobResponse_trainingJobName :: Lens' DescribeTrainingJobResponse Text

Name of the model training job.

describeTrainingJobResponse_trainingJobArn :: Lens' DescribeTrainingJobResponse Text

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

describeTrainingJobResponse_modelArtifacts :: Lens' DescribeTrainingJobResponse ModelArtifacts

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

describeTrainingJobResponse_trainingJobStatus :: Lens' DescribeTrainingJobResponse 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.

describeTrainingJobResponse_secondaryStatus :: Lens' 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.

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

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

describeTrainingJobResponse_resourceConfig :: Lens' DescribeTrainingJobResponse ResourceConfig

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

describeTrainingJobResponse_stoppingCondition :: Lens' 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, 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.

describeTrainingJobResponse_creationTime :: Lens' DescribeTrainingJobResponse UTCTime

A timestamp that indicates when the training job was created.