amazonka-sagemaker-2.0: gen/Amazonka/SageMaker/DescribeTrainingJob.hs
{-# LANGUAGE DeriveGeneric #-}
{-# LANGUAGE DuplicateRecordFields #-}
{-# LANGUAGE NamedFieldPuns #-}
{-# LANGUAGE OverloadedStrings #-}
{-# LANGUAGE RecordWildCards #-}
{-# LANGUAGE StrictData #-}
{-# LANGUAGE TypeFamilies #-}
{-# LANGUAGE NoImplicitPrelude #-}
{-# OPTIONS_GHC -fno-warn-unused-binds #-}
{-# OPTIONS_GHC -fno-warn-unused-imports #-}
{-# OPTIONS_GHC -fno-warn-unused-matches #-}
-- Derived from AWS service descriptions, licensed under Apache 2.0.
-- |
-- Module : Amazonka.SageMaker.DescribeTrainingJob
-- Copyright : (c) 2013-2023 Brendan Hay
-- License : Mozilla Public License, v. 2.0.
-- Maintainer : Brendan Hay
-- Stability : auto-generated
-- Portability : non-portable (GHC extensions)
--
-- 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.
module Amazonka.SageMaker.DescribeTrainingJob
( -- * Creating a Request
DescribeTrainingJob (..),
newDescribeTrainingJob,
-- * Request Lenses
describeTrainingJob_trainingJobName,
-- * Destructuring the Response
DescribeTrainingJobResponse (..),
newDescribeTrainingJobResponse,
-- * Response Lenses
describeTrainingJobResponse_autoMLJobArn,
describeTrainingJobResponse_billableTimeInSeconds,
describeTrainingJobResponse_checkpointConfig,
describeTrainingJobResponse_debugHookConfig,
describeTrainingJobResponse_debugRuleConfigurations,
describeTrainingJobResponse_debugRuleEvaluationStatuses,
describeTrainingJobResponse_enableInterContainerTrafficEncryption,
describeTrainingJobResponse_enableManagedSpotTraining,
describeTrainingJobResponse_enableNetworkIsolation,
describeTrainingJobResponse_environment,
describeTrainingJobResponse_experimentConfig,
describeTrainingJobResponse_failureReason,
describeTrainingJobResponse_finalMetricDataList,
describeTrainingJobResponse_hyperParameters,
describeTrainingJobResponse_inputDataConfig,
describeTrainingJobResponse_labelingJobArn,
describeTrainingJobResponse_lastModifiedTime,
describeTrainingJobResponse_outputDataConfig,
describeTrainingJobResponse_profilerConfig,
describeTrainingJobResponse_profilerRuleConfigurations,
describeTrainingJobResponse_profilerRuleEvaluationStatuses,
describeTrainingJobResponse_profilingStatus,
describeTrainingJobResponse_retryStrategy,
describeTrainingJobResponse_roleArn,
describeTrainingJobResponse_secondaryStatusTransitions,
describeTrainingJobResponse_tensorBoardOutputConfig,
describeTrainingJobResponse_trainingEndTime,
describeTrainingJobResponse_trainingStartTime,
describeTrainingJobResponse_trainingTimeInSeconds,
describeTrainingJobResponse_tuningJobArn,
describeTrainingJobResponse_vpcConfig,
describeTrainingJobResponse_warmPoolStatus,
describeTrainingJobResponse_httpStatus,
describeTrainingJobResponse_trainingJobName,
describeTrainingJobResponse_trainingJobArn,
describeTrainingJobResponse_modelArtifacts,
describeTrainingJobResponse_trainingJobStatus,
describeTrainingJobResponse_secondaryStatus,
describeTrainingJobResponse_algorithmSpecification,
describeTrainingJobResponse_resourceConfig,
describeTrainingJobResponse_stoppingCondition,
describeTrainingJobResponse_creationTime,
)
where
import qualified Amazonka.Core as Core
import qualified Amazonka.Core.Lens.Internal as Lens
import qualified Amazonka.Data as Data
import qualified Amazonka.Prelude as Prelude
import qualified Amazonka.Request as Request
import qualified Amazonka.Response as Response
import Amazonka.SageMaker.Types
-- | /See:/ 'newDescribeTrainingJob' smart constructor.
data DescribeTrainingJob = DescribeTrainingJob'
{ -- | The name of the training job.
trainingJobName :: Prelude.Text
}
deriving (Prelude.Eq, Prelude.Read, Prelude.Show, Prelude.Generic)
-- |
-- Create a value of 'DescribeTrainingJob' with all optional fields omitted.
--
-- Use <https://hackage.haskell.org/package/generic-lens generic-lens> or <https://hackage.haskell.org/package/optics optics> to modify other optional fields.
--
-- The following record fields are available, with the corresponding lenses provided
-- for backwards compatibility:
--
-- 'trainingJobName', 'describeTrainingJob_trainingJobName' - The name of the training job.
newDescribeTrainingJob ::
-- | 'trainingJobName'
Prelude.Text ->
DescribeTrainingJob
newDescribeTrainingJob pTrainingJobName_ =
DescribeTrainingJob'
{ trainingJobName =
pTrainingJobName_
}
-- | The name of the training job.
describeTrainingJob_trainingJobName :: Lens.Lens' DescribeTrainingJob Prelude.Text
describeTrainingJob_trainingJobName = Lens.lens (\DescribeTrainingJob' {trainingJobName} -> trainingJobName) (\s@DescribeTrainingJob' {} a -> s {trainingJobName = a} :: DescribeTrainingJob)
instance Core.AWSRequest DescribeTrainingJob where
type
AWSResponse DescribeTrainingJob =
DescribeTrainingJobResponse
request overrides =
Request.postJSON (overrides defaultService)
response =
Response.receiveJSON
( \s h x ->
DescribeTrainingJobResponse'
Prelude.<$> (x Data..?> "AutoMLJobArn")
Prelude.<*> (x Data..?> "BillableTimeInSeconds")
Prelude.<*> (x Data..?> "CheckpointConfig")
Prelude.<*> (x Data..?> "DebugHookConfig")
Prelude.<*> ( x
Data..?> "DebugRuleConfigurations"
Core..!@ Prelude.mempty
)
Prelude.<*> ( x
Data..?> "DebugRuleEvaluationStatuses"
Core..!@ Prelude.mempty
)
Prelude.<*> (x Data..?> "EnableInterContainerTrafficEncryption")
Prelude.<*> (x Data..?> "EnableManagedSpotTraining")
Prelude.<*> (x Data..?> "EnableNetworkIsolation")
Prelude.<*> (x Data..?> "Environment" Core..!@ Prelude.mempty)
Prelude.<*> (x Data..?> "ExperimentConfig")
Prelude.<*> (x Data..?> "FailureReason")
Prelude.<*> ( x
Data..?> "FinalMetricDataList"
Core..!@ Prelude.mempty
)
Prelude.<*> ( x
Data..?> "HyperParameters"
Core..!@ Prelude.mempty
)
Prelude.<*> (x Data..?> "InputDataConfig")
Prelude.<*> (x Data..?> "LabelingJobArn")
Prelude.<*> (x Data..?> "LastModifiedTime")
Prelude.<*> (x Data..?> "OutputDataConfig")
Prelude.<*> (x Data..?> "ProfilerConfig")
Prelude.<*> ( x
Data..?> "ProfilerRuleConfigurations"
Core..!@ Prelude.mempty
)
Prelude.<*> ( x
Data..?> "ProfilerRuleEvaluationStatuses"
Core..!@ Prelude.mempty
)
Prelude.<*> (x Data..?> "ProfilingStatus")
Prelude.<*> (x Data..?> "RetryStrategy")
Prelude.<*> (x Data..?> "RoleArn")
Prelude.<*> ( x
Data..?> "SecondaryStatusTransitions"
Core..!@ Prelude.mempty
)
Prelude.<*> (x Data..?> "TensorBoardOutputConfig")
Prelude.<*> (x Data..?> "TrainingEndTime")
Prelude.<*> (x Data..?> "TrainingStartTime")
Prelude.<*> (x Data..?> "TrainingTimeInSeconds")
Prelude.<*> (x Data..?> "TuningJobArn")
Prelude.<*> (x Data..?> "VpcConfig")
Prelude.<*> (x Data..?> "WarmPoolStatus")
Prelude.<*> (Prelude.pure (Prelude.fromEnum s))
Prelude.<*> (x Data..:> "TrainingJobName")
Prelude.<*> (x Data..:> "TrainingJobArn")
Prelude.<*> (x Data..:> "ModelArtifacts")
Prelude.<*> (x Data..:> "TrainingJobStatus")
Prelude.<*> (x Data..:> "SecondaryStatus")
Prelude.<*> (x Data..:> "AlgorithmSpecification")
Prelude.<*> (x Data..:> "ResourceConfig")
Prelude.<*> (x Data..:> "StoppingCondition")
Prelude.<*> (x Data..:> "CreationTime")
)
instance Prelude.Hashable DescribeTrainingJob where
hashWithSalt _salt DescribeTrainingJob' {..} =
_salt `Prelude.hashWithSalt` trainingJobName
instance Prelude.NFData DescribeTrainingJob where
rnf DescribeTrainingJob' {..} =
Prelude.rnf trainingJobName
instance Data.ToHeaders DescribeTrainingJob where
toHeaders =
Prelude.const
( Prelude.mconcat
[ "X-Amz-Target"
Data.=# ( "SageMaker.DescribeTrainingJob" ::
Prelude.ByteString
),
"Content-Type"
Data.=# ( "application/x-amz-json-1.1" ::
Prelude.ByteString
)
]
)
instance Data.ToJSON DescribeTrainingJob where
toJSON DescribeTrainingJob' {..} =
Data.object
( Prelude.catMaybes
[ Prelude.Just
("TrainingJobName" Data..= trainingJobName)
]
)
instance Data.ToPath DescribeTrainingJob where
toPath = Prelude.const "/"
instance Data.ToQuery DescribeTrainingJob where
toQuery = Prelude.const Prelude.mempty
-- | /See:/ 'newDescribeTrainingJobResponse' smart constructor.
data DescribeTrainingJobResponse = DescribeTrainingJobResponse'
{ -- | The Amazon Resource Name (ARN) of an AutoML job.
autoMLJobArn :: Prelude.Maybe Prelude.Text,
-- | 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%.
billableTimeInSeconds :: Prelude.Maybe Prelude.Natural,
checkpointConfig :: Prelude.Maybe CheckpointConfig,
debugHookConfig :: Prelude.Maybe DebugHookConfig,
-- | Configuration information for Amazon SageMaker Debugger rules for
-- debugging output tensors.
debugRuleConfigurations :: Prelude.Maybe [DebugRuleConfiguration],
-- | Evaluation status of Amazon SageMaker Debugger rules for debugging on a
-- training job.
debugRuleEvaluationStatuses :: Prelude.Maybe [DebugRuleEvaluationStatus],
-- | 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.
enableInterContainerTrafficEncryption :: Prelude.Maybe Prelude.Bool,
-- | A Boolean indicating whether managed spot training is enabled (@True@)
-- or not (@False@).
enableManagedSpotTraining :: Prelude.Maybe Prelude.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.
enableNetworkIsolation :: Prelude.Maybe Prelude.Bool,
-- | The environment variables to set in the Docker container.
environment :: Prelude.Maybe (Prelude.HashMap Prelude.Text Prelude.Text),
experimentConfig :: Prelude.Maybe ExperimentConfig,
-- | If the training job failed, the reason it failed.
failureReason :: Prelude.Maybe Prelude.Text,
-- | A collection of @MetricData@ objects that specify the names, values, and
-- dates and times that the training algorithm emitted to Amazon
-- CloudWatch.
finalMetricDataList :: Prelude.Maybe [MetricData],
-- | Algorithm-specific parameters.
hyperParameters :: Prelude.Maybe (Prelude.HashMap Prelude.Text Prelude.Text),
-- | An array of @Channel@ objects that describes each data input channel.
inputDataConfig :: Prelude.Maybe (Prelude.NonEmpty Channel),
-- | The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling
-- job that created the transform or training job.
labelingJobArn :: Prelude.Maybe Prelude.Text,
-- | A timestamp that indicates when the status of the training job was last
-- modified.
lastModifiedTime :: Prelude.Maybe Data.POSIX,
-- | The S3 path where model artifacts that you configured when creating the
-- job are stored. SageMaker creates subfolders for model artifacts.
outputDataConfig :: Prelude.Maybe OutputDataConfig,
profilerConfig :: Prelude.Maybe ProfilerConfig,
-- | Configuration information for Amazon SageMaker Debugger rules for
-- profiling system and framework metrics.
profilerRuleConfigurations :: Prelude.Maybe [ProfilerRuleConfiguration],
-- | Evaluation status of Amazon SageMaker Debugger rules for profiling on a
-- training job.
profilerRuleEvaluationStatuses :: Prelude.Maybe [ProfilerRuleEvaluationStatus],
-- | Profiling status of a training job.
profilingStatus :: Prelude.Maybe ProfilingStatus,
-- | The number of times to retry the job when the job fails due to an
-- @InternalServerError@.
retryStrategy :: Prelude.Maybe RetryStrategy,
-- | The Amazon Web Services Identity and Access Management (IAM) role
-- configured for the training job.
roleArn :: Prelude.Maybe Prelude.Text,
-- | A history of all of the secondary statuses that the training job has
-- transitioned through.
secondaryStatusTransitions :: Prelude.Maybe [SecondaryStatusTransition],
tensorBoardOutputConfig :: Prelude.Maybe TensorBoardOutputConfig,
-- | 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.
trainingEndTime :: Prelude.Maybe Data.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.
trainingStartTime :: Prelude.Maybe Data.POSIX,
-- | The training time in seconds.
trainingTimeInSeconds :: Prelude.Maybe Prelude.Natural,
-- | The Amazon Resource Name (ARN) of the associated hyperparameter tuning
-- job if the training job was launched by a hyperparameter tuning job.
tuningJobArn :: Prelude.Maybe Prelude.Text,
-- | A VpcConfig object that specifies the VPC that this training job has
-- access to. For more information, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html Protect Training Jobs by Using an Amazon Virtual Private Cloud>.
vpcConfig :: Prelude.Maybe VpcConfig,
-- | The status of the warm pool associated with the training job.
warmPoolStatus :: Prelude.Maybe WarmPoolStatus,
-- | The response's http status code.
httpStatus :: Prelude.Int,
-- | Name of the model training job.
trainingJobName :: Prelude.Text,
-- | The Amazon Resource Name (ARN) of the training job.
trainingJobArn :: Prelude.Text,
-- | Information about the Amazon S3 location that is configured for storing
-- model artifacts.
modelArtifacts :: ModelArtifacts,
-- | 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@.
trainingJobStatus :: TrainingJobStatus,
-- | 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@
secondaryStatus :: SecondaryStatus,
-- | Information about the algorithm used for training, and algorithm
-- metadata.
algorithmSpecification :: AlgorithmSpecification,
-- | Resources, including ML compute instances and ML storage volumes, that
-- are configured for model training.
resourceConfig :: ResourceConfig,
-- | 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.
stoppingCondition :: StoppingCondition,
-- | A timestamp that indicates when the training job was created.
creationTime :: Data.POSIX
}
deriving (Prelude.Eq, Prelude.Read, Prelude.Show, Prelude.Generic)
-- |
-- Create a value of 'DescribeTrainingJobResponse' with all optional fields omitted.
--
-- Use <https://hackage.haskell.org/package/generic-lens generic-lens> or <https://hackage.haskell.org/package/optics optics> to modify other optional fields.
--
-- The following record fields are available, with the corresponding lenses provided
-- for backwards compatibility:
--
-- 'autoMLJobArn', 'describeTrainingJobResponse_autoMLJobArn' - The Amazon Resource Name (ARN) of an AutoML job.
--
-- 'billableTimeInSeconds', '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%.
--
-- 'checkpointConfig', 'describeTrainingJobResponse_checkpointConfig' - Undocumented member.
--
-- 'debugHookConfig', 'describeTrainingJobResponse_debugHookConfig' - Undocumented member.
--
-- 'debugRuleConfigurations', 'describeTrainingJobResponse_debugRuleConfigurations' - Configuration information for Amazon SageMaker Debugger rules for
-- debugging output tensors.
--
-- 'debugRuleEvaluationStatuses', 'describeTrainingJobResponse_debugRuleEvaluationStatuses' - Evaluation status of Amazon SageMaker Debugger rules for debugging on a
-- training job.
--
-- 'enableInterContainerTrafficEncryption', '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.
--
-- 'enableManagedSpotTraining', 'describeTrainingJobResponse_enableManagedSpotTraining' - A Boolean indicating whether managed spot training is enabled (@True@)
-- or not (@False@).
--
-- 'enableNetworkIsolation', '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.
--
-- 'environment', 'describeTrainingJobResponse_environment' - The environment variables to set in the Docker container.
--
-- 'experimentConfig', 'describeTrainingJobResponse_experimentConfig' - Undocumented member.
--
-- 'failureReason', 'describeTrainingJobResponse_failureReason' - If the training job failed, the reason it failed.
--
-- 'finalMetricDataList', 'describeTrainingJobResponse_finalMetricDataList' - A collection of @MetricData@ objects that specify the names, values, and
-- dates and times that the training algorithm emitted to Amazon
-- CloudWatch.
--
-- 'hyperParameters', 'describeTrainingJobResponse_hyperParameters' - Algorithm-specific parameters.
--
-- 'inputDataConfig', 'describeTrainingJobResponse_inputDataConfig' - An array of @Channel@ objects that describes each data input channel.
--
-- 'labelingJobArn', 'describeTrainingJobResponse_labelingJobArn' - The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling
-- job that created the transform or training job.
--
-- 'lastModifiedTime', 'describeTrainingJobResponse_lastModifiedTime' - A timestamp that indicates when the status of the training job was last
-- modified.
--
-- 'outputDataConfig', 'describeTrainingJobResponse_outputDataConfig' - The S3 path where model artifacts that you configured when creating the
-- job are stored. SageMaker creates subfolders for model artifacts.
--
-- 'profilerConfig', 'describeTrainingJobResponse_profilerConfig' - Undocumented member.
--
-- 'profilerRuleConfigurations', 'describeTrainingJobResponse_profilerRuleConfigurations' - Configuration information for Amazon SageMaker Debugger rules for
-- profiling system and framework metrics.
--
-- 'profilerRuleEvaluationStatuses', 'describeTrainingJobResponse_profilerRuleEvaluationStatuses' - Evaluation status of Amazon SageMaker Debugger rules for profiling on a
-- training job.
--
-- 'profilingStatus', 'describeTrainingJobResponse_profilingStatus' - Profiling status of a training job.
--
-- 'retryStrategy', 'describeTrainingJobResponse_retryStrategy' - The number of times to retry the job when the job fails due to an
-- @InternalServerError@.
--
-- 'roleArn', 'describeTrainingJobResponse_roleArn' - The Amazon Web Services Identity and Access Management (IAM) role
-- configured for the training job.
--
-- 'secondaryStatusTransitions', 'describeTrainingJobResponse_secondaryStatusTransitions' - A history of all of the secondary statuses that the training job has
-- transitioned through.
--
-- 'tensorBoardOutputConfig', 'describeTrainingJobResponse_tensorBoardOutputConfig' - Undocumented member.
--
-- 'trainingEndTime', '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.
--
-- 'trainingStartTime', '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.
--
-- 'trainingTimeInSeconds', 'describeTrainingJobResponse_trainingTimeInSeconds' - The training time in seconds.
--
-- 'tuningJobArn', 'describeTrainingJobResponse_tuningJobArn' - The Amazon Resource Name (ARN) of the associated hyperparameter tuning
-- job if the training job was launched by a hyperparameter tuning job.
--
-- 'vpcConfig', 'describeTrainingJobResponse_vpcConfig' - A VpcConfig object that specifies the VPC that this training job has
-- access to. For more information, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html Protect Training Jobs by Using an Amazon Virtual Private Cloud>.
--
-- 'warmPoolStatus', 'describeTrainingJobResponse_warmPoolStatus' - The status of the warm pool associated with the training job.
--
-- 'httpStatus', 'describeTrainingJobResponse_httpStatus' - The response's http status code.
--
-- 'trainingJobName', 'describeTrainingJobResponse_trainingJobName' - Name of the model training job.
--
-- 'trainingJobArn', 'describeTrainingJobResponse_trainingJobArn' - The Amazon Resource Name (ARN) of the training job.
--
-- 'modelArtifacts', 'describeTrainingJobResponse_modelArtifacts' - Information about the Amazon S3 location that is configured for storing
-- model artifacts.
--
-- 'trainingJobStatus', '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@.
--
-- 'secondaryStatus', '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@
--
-- 'algorithmSpecification', 'describeTrainingJobResponse_algorithmSpecification' - Information about the algorithm used for training, and algorithm
-- metadata.
--
-- 'resourceConfig', 'describeTrainingJobResponse_resourceConfig' - Resources, including ML compute instances and ML storage volumes, that
-- are configured for model training.
--
-- 'stoppingCondition', '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.
--
-- 'creationTime', 'describeTrainingJobResponse_creationTime' - A timestamp that indicates when the training job was created.
newDescribeTrainingJobResponse ::
-- | 'httpStatus'
Prelude.Int ->
-- | 'trainingJobName'
Prelude.Text ->
-- | 'trainingJobArn'
Prelude.Text ->
-- | 'modelArtifacts'
ModelArtifacts ->
-- | 'trainingJobStatus'
TrainingJobStatus ->
-- | 'secondaryStatus'
SecondaryStatus ->
-- | 'algorithmSpecification'
AlgorithmSpecification ->
-- | 'resourceConfig'
ResourceConfig ->
-- | 'stoppingCondition'
StoppingCondition ->
-- | 'creationTime'
Prelude.UTCTime ->
DescribeTrainingJobResponse
newDescribeTrainingJobResponse
pHttpStatus_
pTrainingJobName_
pTrainingJobArn_
pModelArtifacts_
pTrainingJobStatus_
pSecondaryStatus_
pAlgorithmSpecification_
pResourceConfig_
pStoppingCondition_
pCreationTime_ =
DescribeTrainingJobResponse'
{ autoMLJobArn =
Prelude.Nothing,
billableTimeInSeconds = Prelude.Nothing,
checkpointConfig = Prelude.Nothing,
debugHookConfig = Prelude.Nothing,
debugRuleConfigurations = Prelude.Nothing,
debugRuleEvaluationStatuses = Prelude.Nothing,
enableInterContainerTrafficEncryption =
Prelude.Nothing,
enableManagedSpotTraining = Prelude.Nothing,
enableNetworkIsolation = Prelude.Nothing,
environment = Prelude.Nothing,
experimentConfig = Prelude.Nothing,
failureReason = Prelude.Nothing,
finalMetricDataList = Prelude.Nothing,
hyperParameters = Prelude.Nothing,
inputDataConfig = Prelude.Nothing,
labelingJobArn = Prelude.Nothing,
lastModifiedTime = Prelude.Nothing,
outputDataConfig = Prelude.Nothing,
profilerConfig = Prelude.Nothing,
profilerRuleConfigurations = Prelude.Nothing,
profilerRuleEvaluationStatuses =
Prelude.Nothing,
profilingStatus = Prelude.Nothing,
retryStrategy = Prelude.Nothing,
roleArn = Prelude.Nothing,
secondaryStatusTransitions = Prelude.Nothing,
tensorBoardOutputConfig = Prelude.Nothing,
trainingEndTime = Prelude.Nothing,
trainingStartTime = Prelude.Nothing,
trainingTimeInSeconds = Prelude.Nothing,
tuningJobArn = Prelude.Nothing,
vpcConfig = Prelude.Nothing,
warmPoolStatus = Prelude.Nothing,
httpStatus = pHttpStatus_,
trainingJobName = pTrainingJobName_,
trainingJobArn = pTrainingJobArn_,
modelArtifacts = pModelArtifacts_,
trainingJobStatus = pTrainingJobStatus_,
secondaryStatus = pSecondaryStatus_,
algorithmSpecification =
pAlgorithmSpecification_,
resourceConfig = pResourceConfig_,
stoppingCondition = pStoppingCondition_,
creationTime =
Data._Time Lens.# pCreationTime_
}
-- | The Amazon Resource Name (ARN) of an AutoML job.
describeTrainingJobResponse_autoMLJobArn :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe Prelude.Text)
describeTrainingJobResponse_autoMLJobArn = Lens.lens (\DescribeTrainingJobResponse' {autoMLJobArn} -> autoMLJobArn) (\s@DescribeTrainingJobResponse' {} a -> s {autoMLJobArn = a} :: DescribeTrainingJobResponse)
-- | 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_billableTimeInSeconds :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe Prelude.Natural)
describeTrainingJobResponse_billableTimeInSeconds = Lens.lens (\DescribeTrainingJobResponse' {billableTimeInSeconds} -> billableTimeInSeconds) (\s@DescribeTrainingJobResponse' {} a -> s {billableTimeInSeconds = a} :: DescribeTrainingJobResponse)
-- | Undocumented member.
describeTrainingJobResponse_checkpointConfig :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe CheckpointConfig)
describeTrainingJobResponse_checkpointConfig = Lens.lens (\DescribeTrainingJobResponse' {checkpointConfig} -> checkpointConfig) (\s@DescribeTrainingJobResponse' {} a -> s {checkpointConfig = a} :: DescribeTrainingJobResponse)
-- | Undocumented member.
describeTrainingJobResponse_debugHookConfig :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe DebugHookConfig)
describeTrainingJobResponse_debugHookConfig = Lens.lens (\DescribeTrainingJobResponse' {debugHookConfig} -> debugHookConfig) (\s@DescribeTrainingJobResponse' {} a -> s {debugHookConfig = a} :: DescribeTrainingJobResponse)
-- | Configuration information for Amazon SageMaker Debugger rules for
-- debugging output tensors.
describeTrainingJobResponse_debugRuleConfigurations :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe [DebugRuleConfiguration])
describeTrainingJobResponse_debugRuleConfigurations = Lens.lens (\DescribeTrainingJobResponse' {debugRuleConfigurations} -> debugRuleConfigurations) (\s@DescribeTrainingJobResponse' {} a -> s {debugRuleConfigurations = a} :: DescribeTrainingJobResponse) Prelude.. Lens.mapping Lens.coerced
-- | Evaluation status of Amazon SageMaker Debugger rules for debugging on a
-- training job.
describeTrainingJobResponse_debugRuleEvaluationStatuses :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe [DebugRuleEvaluationStatus])
describeTrainingJobResponse_debugRuleEvaluationStatuses = Lens.lens (\DescribeTrainingJobResponse' {debugRuleEvaluationStatuses} -> debugRuleEvaluationStatuses) (\s@DescribeTrainingJobResponse' {} a -> s {debugRuleEvaluationStatuses = a} :: DescribeTrainingJobResponse) Prelude.. Lens.mapping Lens.coerced
-- | 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_enableInterContainerTrafficEncryption :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe Prelude.Bool)
describeTrainingJobResponse_enableInterContainerTrafficEncryption = Lens.lens (\DescribeTrainingJobResponse' {enableInterContainerTrafficEncryption} -> enableInterContainerTrafficEncryption) (\s@DescribeTrainingJobResponse' {} a -> s {enableInterContainerTrafficEncryption = a} :: DescribeTrainingJobResponse)
-- | A Boolean indicating whether managed spot training is enabled (@True@)
-- or not (@False@).
describeTrainingJobResponse_enableManagedSpotTraining :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe Prelude.Bool)
describeTrainingJobResponse_enableManagedSpotTraining = Lens.lens (\DescribeTrainingJobResponse' {enableManagedSpotTraining} -> enableManagedSpotTraining) (\s@DescribeTrainingJobResponse' {} a -> s {enableManagedSpotTraining = a} :: DescribeTrainingJobResponse)
-- | 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_enableNetworkIsolation :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe Prelude.Bool)
describeTrainingJobResponse_enableNetworkIsolation = Lens.lens (\DescribeTrainingJobResponse' {enableNetworkIsolation} -> enableNetworkIsolation) (\s@DescribeTrainingJobResponse' {} a -> s {enableNetworkIsolation = a} :: DescribeTrainingJobResponse)
-- | The environment variables to set in the Docker container.
describeTrainingJobResponse_environment :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe (Prelude.HashMap Prelude.Text Prelude.Text))
describeTrainingJobResponse_environment = Lens.lens (\DescribeTrainingJobResponse' {environment} -> environment) (\s@DescribeTrainingJobResponse' {} a -> s {environment = a} :: DescribeTrainingJobResponse) Prelude.. Lens.mapping Lens.coerced
-- | Undocumented member.
describeTrainingJobResponse_experimentConfig :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe ExperimentConfig)
describeTrainingJobResponse_experimentConfig = Lens.lens (\DescribeTrainingJobResponse' {experimentConfig} -> experimentConfig) (\s@DescribeTrainingJobResponse' {} a -> s {experimentConfig = a} :: DescribeTrainingJobResponse)
-- | If the training job failed, the reason it failed.
describeTrainingJobResponse_failureReason :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe Prelude.Text)
describeTrainingJobResponse_failureReason = Lens.lens (\DescribeTrainingJobResponse' {failureReason} -> failureReason) (\s@DescribeTrainingJobResponse' {} a -> s {failureReason = a} :: DescribeTrainingJobResponse)
-- | A collection of @MetricData@ objects that specify the names, values, and
-- dates and times that the training algorithm emitted to Amazon
-- CloudWatch.
describeTrainingJobResponse_finalMetricDataList :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe [MetricData])
describeTrainingJobResponse_finalMetricDataList = Lens.lens (\DescribeTrainingJobResponse' {finalMetricDataList} -> finalMetricDataList) (\s@DescribeTrainingJobResponse' {} a -> s {finalMetricDataList = a} :: DescribeTrainingJobResponse) Prelude.. Lens.mapping Lens.coerced
-- | Algorithm-specific parameters.
describeTrainingJobResponse_hyperParameters :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe (Prelude.HashMap Prelude.Text Prelude.Text))
describeTrainingJobResponse_hyperParameters = Lens.lens (\DescribeTrainingJobResponse' {hyperParameters} -> hyperParameters) (\s@DescribeTrainingJobResponse' {} a -> s {hyperParameters = a} :: DescribeTrainingJobResponse) Prelude.. Lens.mapping Lens.coerced
-- | An array of @Channel@ objects that describes each data input channel.
describeTrainingJobResponse_inputDataConfig :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe (Prelude.NonEmpty Channel))
describeTrainingJobResponse_inputDataConfig = Lens.lens (\DescribeTrainingJobResponse' {inputDataConfig} -> inputDataConfig) (\s@DescribeTrainingJobResponse' {} a -> s {inputDataConfig = a} :: DescribeTrainingJobResponse) Prelude.. Lens.mapping Lens.coerced
-- | The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling
-- job that created the transform or training job.
describeTrainingJobResponse_labelingJobArn :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe Prelude.Text)
describeTrainingJobResponse_labelingJobArn = Lens.lens (\DescribeTrainingJobResponse' {labelingJobArn} -> labelingJobArn) (\s@DescribeTrainingJobResponse' {} a -> s {labelingJobArn = a} :: DescribeTrainingJobResponse)
-- | A timestamp that indicates when the status of the training job was last
-- modified.
describeTrainingJobResponse_lastModifiedTime :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe Prelude.UTCTime)
describeTrainingJobResponse_lastModifiedTime = Lens.lens (\DescribeTrainingJobResponse' {lastModifiedTime} -> lastModifiedTime) (\s@DescribeTrainingJobResponse' {} a -> s {lastModifiedTime = a} :: DescribeTrainingJobResponse) Prelude.. Lens.mapping Data._Time
-- | The S3 path where model artifacts that you configured when creating the
-- job are stored. SageMaker creates subfolders for model artifacts.
describeTrainingJobResponse_outputDataConfig :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe OutputDataConfig)
describeTrainingJobResponse_outputDataConfig = Lens.lens (\DescribeTrainingJobResponse' {outputDataConfig} -> outputDataConfig) (\s@DescribeTrainingJobResponse' {} a -> s {outputDataConfig = a} :: DescribeTrainingJobResponse)
-- | Undocumented member.
describeTrainingJobResponse_profilerConfig :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe ProfilerConfig)
describeTrainingJobResponse_profilerConfig = Lens.lens (\DescribeTrainingJobResponse' {profilerConfig} -> profilerConfig) (\s@DescribeTrainingJobResponse' {} a -> s {profilerConfig = a} :: DescribeTrainingJobResponse)
-- | Configuration information for Amazon SageMaker Debugger rules for
-- profiling system and framework metrics.
describeTrainingJobResponse_profilerRuleConfigurations :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe [ProfilerRuleConfiguration])
describeTrainingJobResponse_profilerRuleConfigurations = Lens.lens (\DescribeTrainingJobResponse' {profilerRuleConfigurations} -> profilerRuleConfigurations) (\s@DescribeTrainingJobResponse' {} a -> s {profilerRuleConfigurations = a} :: DescribeTrainingJobResponse) Prelude.. Lens.mapping Lens.coerced
-- | Evaluation status of Amazon SageMaker Debugger rules for profiling on a
-- training job.
describeTrainingJobResponse_profilerRuleEvaluationStatuses :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe [ProfilerRuleEvaluationStatus])
describeTrainingJobResponse_profilerRuleEvaluationStatuses = Lens.lens (\DescribeTrainingJobResponse' {profilerRuleEvaluationStatuses} -> profilerRuleEvaluationStatuses) (\s@DescribeTrainingJobResponse' {} a -> s {profilerRuleEvaluationStatuses = a} :: DescribeTrainingJobResponse) Prelude.. Lens.mapping Lens.coerced
-- | Profiling status of a training job.
describeTrainingJobResponse_profilingStatus :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe ProfilingStatus)
describeTrainingJobResponse_profilingStatus = Lens.lens (\DescribeTrainingJobResponse' {profilingStatus} -> profilingStatus) (\s@DescribeTrainingJobResponse' {} a -> s {profilingStatus = a} :: DescribeTrainingJobResponse)
-- | The number of times to retry the job when the job fails due to an
-- @InternalServerError@.
describeTrainingJobResponse_retryStrategy :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe RetryStrategy)
describeTrainingJobResponse_retryStrategy = Lens.lens (\DescribeTrainingJobResponse' {retryStrategy} -> retryStrategy) (\s@DescribeTrainingJobResponse' {} a -> s {retryStrategy = a} :: DescribeTrainingJobResponse)
-- | The Amazon Web Services Identity and Access Management (IAM) role
-- configured for the training job.
describeTrainingJobResponse_roleArn :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe Prelude.Text)
describeTrainingJobResponse_roleArn = Lens.lens (\DescribeTrainingJobResponse' {roleArn} -> roleArn) (\s@DescribeTrainingJobResponse' {} a -> s {roleArn = a} :: DescribeTrainingJobResponse)
-- | A history of all of the secondary statuses that the training job has
-- transitioned through.
describeTrainingJobResponse_secondaryStatusTransitions :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe [SecondaryStatusTransition])
describeTrainingJobResponse_secondaryStatusTransitions = Lens.lens (\DescribeTrainingJobResponse' {secondaryStatusTransitions} -> secondaryStatusTransitions) (\s@DescribeTrainingJobResponse' {} a -> s {secondaryStatusTransitions = a} :: DescribeTrainingJobResponse) Prelude.. Lens.mapping Lens.coerced
-- | Undocumented member.
describeTrainingJobResponse_tensorBoardOutputConfig :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe TensorBoardOutputConfig)
describeTrainingJobResponse_tensorBoardOutputConfig = Lens.lens (\DescribeTrainingJobResponse' {tensorBoardOutputConfig} -> tensorBoardOutputConfig) (\s@DescribeTrainingJobResponse' {} a -> s {tensorBoardOutputConfig = a} :: DescribeTrainingJobResponse)
-- | 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_trainingEndTime :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe Prelude.UTCTime)
describeTrainingJobResponse_trainingEndTime = Lens.lens (\DescribeTrainingJobResponse' {trainingEndTime} -> trainingEndTime) (\s@DescribeTrainingJobResponse' {} a -> s {trainingEndTime = a} :: DescribeTrainingJobResponse) Prelude.. Lens.mapping Data._Time
-- | Indicates the time when the training job starts on training instances.
-- You are billed for the time interval between this time and the value of
-- @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_trainingStartTime :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe Prelude.UTCTime)
describeTrainingJobResponse_trainingStartTime = Lens.lens (\DescribeTrainingJobResponse' {trainingStartTime} -> trainingStartTime) (\s@DescribeTrainingJobResponse' {} a -> s {trainingStartTime = a} :: DescribeTrainingJobResponse) Prelude.. Lens.mapping Data._Time
-- | The training time in seconds.
describeTrainingJobResponse_trainingTimeInSeconds :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe Prelude.Natural)
describeTrainingJobResponse_trainingTimeInSeconds = Lens.lens (\DescribeTrainingJobResponse' {trainingTimeInSeconds} -> trainingTimeInSeconds) (\s@DescribeTrainingJobResponse' {} a -> s {trainingTimeInSeconds = a} :: DescribeTrainingJobResponse)
-- | The Amazon Resource Name (ARN) of the associated hyperparameter tuning
-- job if the training job was launched by a hyperparameter tuning job.
describeTrainingJobResponse_tuningJobArn :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe Prelude.Text)
describeTrainingJobResponse_tuningJobArn = Lens.lens (\DescribeTrainingJobResponse' {tuningJobArn} -> tuningJobArn) (\s@DescribeTrainingJobResponse' {} a -> s {tuningJobArn = a} :: DescribeTrainingJobResponse)
-- | A VpcConfig object that specifies the VPC that this training job has
-- access to. For more information, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html Protect Training Jobs by Using an Amazon Virtual Private Cloud>.
describeTrainingJobResponse_vpcConfig :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe VpcConfig)
describeTrainingJobResponse_vpcConfig = Lens.lens (\DescribeTrainingJobResponse' {vpcConfig} -> vpcConfig) (\s@DescribeTrainingJobResponse' {} a -> s {vpcConfig = a} :: DescribeTrainingJobResponse)
-- | The status of the warm pool associated with the training job.
describeTrainingJobResponse_warmPoolStatus :: Lens.Lens' DescribeTrainingJobResponse (Prelude.Maybe WarmPoolStatus)
describeTrainingJobResponse_warmPoolStatus = Lens.lens (\DescribeTrainingJobResponse' {warmPoolStatus} -> warmPoolStatus) (\s@DescribeTrainingJobResponse' {} a -> s {warmPoolStatus = a} :: DescribeTrainingJobResponse)
-- | The response's http status code.
describeTrainingJobResponse_httpStatus :: Lens.Lens' DescribeTrainingJobResponse Prelude.Int
describeTrainingJobResponse_httpStatus = Lens.lens (\DescribeTrainingJobResponse' {httpStatus} -> httpStatus) (\s@DescribeTrainingJobResponse' {} a -> s {httpStatus = a} :: DescribeTrainingJobResponse)
-- | Name of the model training job.
describeTrainingJobResponse_trainingJobName :: Lens.Lens' DescribeTrainingJobResponse Prelude.Text
describeTrainingJobResponse_trainingJobName = Lens.lens (\DescribeTrainingJobResponse' {trainingJobName} -> trainingJobName) (\s@DescribeTrainingJobResponse' {} a -> s {trainingJobName = a} :: DescribeTrainingJobResponse)
-- | The Amazon Resource Name (ARN) of the training job.
describeTrainingJobResponse_trainingJobArn :: Lens.Lens' DescribeTrainingJobResponse Prelude.Text
describeTrainingJobResponse_trainingJobArn = Lens.lens (\DescribeTrainingJobResponse' {trainingJobArn} -> trainingJobArn) (\s@DescribeTrainingJobResponse' {} a -> s {trainingJobArn = a} :: DescribeTrainingJobResponse)
-- | Information about the Amazon S3 location that is configured for storing
-- model artifacts.
describeTrainingJobResponse_modelArtifacts :: Lens.Lens' DescribeTrainingJobResponse ModelArtifacts
describeTrainingJobResponse_modelArtifacts = Lens.lens (\DescribeTrainingJobResponse' {modelArtifacts} -> modelArtifacts) (\s@DescribeTrainingJobResponse' {} a -> s {modelArtifacts = a} :: DescribeTrainingJobResponse)
-- | 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_trainingJobStatus :: Lens.Lens' DescribeTrainingJobResponse TrainingJobStatus
describeTrainingJobResponse_trainingJobStatus = Lens.lens (\DescribeTrainingJobResponse' {trainingJobStatus} -> trainingJobStatus) (\s@DescribeTrainingJobResponse' {} a -> s {trainingJobStatus = a} :: DescribeTrainingJobResponse)
-- | 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_secondaryStatus :: Lens.Lens' DescribeTrainingJobResponse SecondaryStatus
describeTrainingJobResponse_secondaryStatus = Lens.lens (\DescribeTrainingJobResponse' {secondaryStatus} -> secondaryStatus) (\s@DescribeTrainingJobResponse' {} a -> s {secondaryStatus = a} :: DescribeTrainingJobResponse)
-- | Information about the algorithm used for training, and algorithm
-- metadata.
describeTrainingJobResponse_algorithmSpecification :: Lens.Lens' DescribeTrainingJobResponse AlgorithmSpecification
describeTrainingJobResponse_algorithmSpecification = Lens.lens (\DescribeTrainingJobResponse' {algorithmSpecification} -> algorithmSpecification) (\s@DescribeTrainingJobResponse' {} a -> s {algorithmSpecification = a} :: DescribeTrainingJobResponse)
-- | Resources, including ML compute instances and ML storage volumes, that
-- are configured for model training.
describeTrainingJobResponse_resourceConfig :: Lens.Lens' DescribeTrainingJobResponse ResourceConfig
describeTrainingJobResponse_resourceConfig = Lens.lens (\DescribeTrainingJobResponse' {resourceConfig} -> resourceConfig) (\s@DescribeTrainingJobResponse' {} a -> s {resourceConfig = a} :: DescribeTrainingJobResponse)
-- | 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_stoppingCondition :: Lens.Lens' DescribeTrainingJobResponse StoppingCondition
describeTrainingJobResponse_stoppingCondition = Lens.lens (\DescribeTrainingJobResponse' {stoppingCondition} -> stoppingCondition) (\s@DescribeTrainingJobResponse' {} a -> s {stoppingCondition = a} :: DescribeTrainingJobResponse)
-- | A timestamp that indicates when the training job was created.
describeTrainingJobResponse_creationTime :: Lens.Lens' DescribeTrainingJobResponse Prelude.UTCTime
describeTrainingJobResponse_creationTime = Lens.lens (\DescribeTrainingJobResponse' {creationTime} -> creationTime) (\s@DescribeTrainingJobResponse' {} a -> s {creationTime = a} :: DescribeTrainingJobResponse) Prelude.. Data._Time
instance Prelude.NFData DescribeTrainingJobResponse where
rnf DescribeTrainingJobResponse' {..} =
Prelude.rnf autoMLJobArn
`Prelude.seq` Prelude.rnf billableTimeInSeconds
`Prelude.seq` Prelude.rnf checkpointConfig
`Prelude.seq` Prelude.rnf debugHookConfig
`Prelude.seq` Prelude.rnf debugRuleConfigurations
`Prelude.seq` Prelude.rnf debugRuleEvaluationStatuses
`Prelude.seq` Prelude.rnf enableInterContainerTrafficEncryption
`Prelude.seq` Prelude.rnf enableManagedSpotTraining
`Prelude.seq` Prelude.rnf enableNetworkIsolation
`Prelude.seq` Prelude.rnf environment
`Prelude.seq` Prelude.rnf experimentConfig
`Prelude.seq` Prelude.rnf failureReason
`Prelude.seq` Prelude.rnf finalMetricDataList
`Prelude.seq` Prelude.rnf hyperParameters
`Prelude.seq` Prelude.rnf inputDataConfig
`Prelude.seq` Prelude.rnf labelingJobArn
`Prelude.seq` Prelude.rnf lastModifiedTime
`Prelude.seq` Prelude.rnf outputDataConfig
`Prelude.seq` Prelude.rnf profilerConfig
`Prelude.seq` Prelude.rnf
profilerRuleConfigurations
`Prelude.seq` Prelude.rnf
profilerRuleEvaluationStatuses
`Prelude.seq` Prelude.rnf
profilingStatus
`Prelude.seq` Prelude.rnf
retryStrategy
`Prelude.seq` Prelude.rnf roleArn
`Prelude.seq` Prelude.rnf
secondaryStatusTransitions
`Prelude.seq` Prelude.rnf
tensorBoardOutputConfig
`Prelude.seq` Prelude.rnf
trainingEndTime
`Prelude.seq` Prelude.rnf
trainingStartTime
`Prelude.seq` Prelude.rnf
trainingTimeInSeconds
`Prelude.seq` Prelude.rnf
tuningJobArn
`Prelude.seq` Prelude.rnf
vpcConfig
`Prelude.seq` Prelude.rnf
warmPoolStatus
`Prelude.seq` Prelude.rnf
httpStatus
`Prelude.seq` Prelude.rnf
trainingJobName
`Prelude.seq` Prelude.rnf
trainingJobArn
`Prelude.seq` Prelude.rnf
modelArtifacts
`Prelude.seq` Prelude.rnf
trainingJobStatus
`Prelude.seq` Prelude.rnf
secondaryStatus
`Prelude.seq` Prelude.rnf
algorithmSpecification
`Prelude.seq` Prelude.rnf
resourceConfig
`Prelude.seq` Prelude.rnf
stoppingCondition
`Prelude.seq` Prelude.rnf
creationTime