public static interface DescribeTrainingJobResponse.Builder extends SageMakerResponse.Builder, SdkPojo, CopyableBuilder<DescribeTrainingJobResponse.Builder,DescribeTrainingJobResponse>
| Modifier and Type | Method and Description |
|---|---|
DescribeTrainingJobResponse.Builder |
algorithmSpecification(AlgorithmSpecification algorithmSpecification)
Information about the algorithm used for training, and algorithm metadata.
|
default DescribeTrainingJobResponse.Builder |
algorithmSpecification(Consumer<AlgorithmSpecification.Builder> algorithmSpecification)
Information about the algorithm used for training, and algorithm metadata.
|
DescribeTrainingJobResponse.Builder |
billableTimeInSeconds(Integer billableTimeInSeconds)
The billable time in seconds.
|
DescribeTrainingJobResponse.Builder |
checkpointConfig(CheckpointConfig checkpointConfig)
Sets the value of the CheckpointConfig property for this object.
|
default DescribeTrainingJobResponse.Builder |
checkpointConfig(Consumer<CheckpointConfig.Builder> checkpointConfig)
Sets the value of the CheckpointConfig property for this object.
|
DescribeTrainingJobResponse.Builder |
creationTime(Instant creationTime)
A timestamp that indicates when the training job was created.
|
DescribeTrainingJobResponse.Builder |
enableInterContainerTrafficEncryption(Boolean enableInterContainerTrafficEncryption)
To encrypt all communications between ML compute instances in distributed training, choose
True. |
DescribeTrainingJobResponse.Builder |
enableManagedSpotTraining(Boolean enableManagedSpotTraining)
A Boolean indicating whether managed spot training is enabled (
True) or not (False
). |
DescribeTrainingJobResponse.Builder |
enableNetworkIsolation(Boolean enableNetworkIsolation)
If you want to allow inbound or outbound network calls, except for calls between peers within a training
cluster for distributed training, choose
True. |
DescribeTrainingJobResponse.Builder |
failureReason(String failureReason)
If the training job failed, the reason it failed.
|
DescribeTrainingJobResponse.Builder |
finalMetricDataList(Collection<MetricData> finalMetricDataList)
A collection of
MetricData objects that specify the names, values, and dates and times that the
training algorithm emitted to Amazon CloudWatch. |
DescribeTrainingJobResponse.Builder |
finalMetricDataList(Consumer<MetricData.Builder>... finalMetricDataList)
A collection of
MetricData objects that specify the names, values, and dates and times that the
training algorithm emitted to Amazon CloudWatch. |
DescribeTrainingJobResponse.Builder |
finalMetricDataList(MetricData... finalMetricDataList)
A collection of
MetricData objects that specify the names, values, and dates and times that the
training algorithm emitted to Amazon CloudWatch. |
DescribeTrainingJobResponse.Builder |
hyperParameters(Map<String,String> hyperParameters)
Algorithm-specific parameters.
|
DescribeTrainingJobResponse.Builder |
inputDataConfig(Channel... inputDataConfig)
An array of
Channel objects that describes each data input channel. |
DescribeTrainingJobResponse.Builder |
inputDataConfig(Collection<Channel> inputDataConfig)
An array of
Channel objects that describes each data input channel. |
DescribeTrainingJobResponse.Builder |
inputDataConfig(Consumer<Channel.Builder>... inputDataConfig)
An array of
Channel objects that describes each data input channel. |
DescribeTrainingJobResponse.Builder |
labelingJobArn(String labelingJobArn)
The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform
or training job.
|
DescribeTrainingJobResponse.Builder |
lastModifiedTime(Instant lastModifiedTime)
A timestamp that indicates when the status of the training job was last modified.
|
default DescribeTrainingJobResponse.Builder |
modelArtifacts(Consumer<ModelArtifacts.Builder> modelArtifacts)
Information about the Amazon S3 location that is configured for storing model artifacts.
|
DescribeTrainingJobResponse.Builder |
modelArtifacts(ModelArtifacts modelArtifacts)
Information about the Amazon S3 location that is configured for storing model artifacts.
|
default DescribeTrainingJobResponse.Builder |
outputDataConfig(Consumer<OutputDataConfig.Builder> outputDataConfig)
The S3 path where model artifacts that you configured when creating the job are stored.
|
DescribeTrainingJobResponse.Builder |
outputDataConfig(OutputDataConfig outputDataConfig)
The S3 path where model artifacts that you configured when creating the job are stored.
|
default DescribeTrainingJobResponse.Builder |
resourceConfig(Consumer<ResourceConfig.Builder> resourceConfig)
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
|
DescribeTrainingJobResponse.Builder |
resourceConfig(ResourceConfig resourceConfig)
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
|
DescribeTrainingJobResponse.Builder |
roleArn(String roleArn)
The AWS Identity and Access Management (IAM) role configured for the training job.
|
DescribeTrainingJobResponse.Builder |
secondaryStatus(SecondaryStatus secondaryStatus)
Provides detailed information about the state of the training job.
|
DescribeTrainingJobResponse.Builder |
secondaryStatus(String secondaryStatus)
Provides detailed information about the state of the training job.
|
DescribeTrainingJobResponse.Builder |
secondaryStatusTransitions(Collection<SecondaryStatusTransition> secondaryStatusTransitions)
A history of all of the secondary statuses that the training job has transitioned through.
|
DescribeTrainingJobResponse.Builder |
secondaryStatusTransitions(Consumer<SecondaryStatusTransition.Builder>... secondaryStatusTransitions)
A history of all of the secondary statuses that the training job has transitioned through.
|
DescribeTrainingJobResponse.Builder |
secondaryStatusTransitions(SecondaryStatusTransition... secondaryStatusTransitions)
A history of all of the secondary statuses that the training job has transitioned through.
|
default DescribeTrainingJobResponse.Builder |
stoppingCondition(Consumer<StoppingCondition.Builder> stoppingCondition)
Specifies a limit to how long a model training job can run.
|
DescribeTrainingJobResponse.Builder |
stoppingCondition(StoppingCondition stoppingCondition)
Specifies a limit to how long a model training job can run.
|
DescribeTrainingJobResponse.Builder |
trainingEndTime(Instant trainingEndTime)
Indicates the time when the training job ends on training instances.
|
DescribeTrainingJobResponse.Builder |
trainingJobArn(String trainingJobArn)
The Amazon Resource Name (ARN) of the training job.
|
DescribeTrainingJobResponse.Builder |
trainingJobName(String trainingJobName)
Name of the model training job.
|
DescribeTrainingJobResponse.Builder |
trainingJobStatus(String trainingJobStatus)
The status of the training job.
|
DescribeTrainingJobResponse.Builder |
trainingJobStatus(TrainingJobStatus trainingJobStatus)
The status of the training job.
|
DescribeTrainingJobResponse.Builder |
trainingStartTime(Instant trainingStartTime)
Indicates the time when the training job starts on training instances.
|
DescribeTrainingJobResponse.Builder |
trainingTimeInSeconds(Integer trainingTimeInSeconds)
The training time in seconds.
|
DescribeTrainingJobResponse.Builder |
tuningJobArn(String tuningJobArn)
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched
by a hyperparameter tuning job.
|
default DescribeTrainingJobResponse.Builder |
vpcConfig(Consumer<VpcConfig.Builder> vpcConfig)
A VpcConfig object that specifies the VPC that this training job has access to.
|
DescribeTrainingJobResponse.Builder |
vpcConfig(VpcConfig vpcConfig)
A VpcConfig object that specifies the VPC that this training job has access to.
|
build, responseMetadata, responseMetadatasdkHttpResponse, sdkHttpResponseequalsBySdkFields, sdkFieldscopyapplyMutation, buildDescribeTrainingJobResponse.Builder trainingJobName(String trainingJobName)
Name of the model training job.
trainingJobName - Name of the model training job.DescribeTrainingJobResponse.Builder trainingJobArn(String trainingJobArn)
The Amazon Resource Name (ARN) of the training job.
trainingJobArn - The Amazon Resource Name (ARN) of the training job.DescribeTrainingJobResponse.Builder tuningJobArn(String tuningJobArn)
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
tuningJobArn - The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was
launched by a hyperparameter tuning job.DescribeTrainingJobResponse.Builder labelingJobArn(String labelingJobArn)
The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
labelingJobArn - The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the
transform or training job.DescribeTrainingJobResponse.Builder modelArtifacts(ModelArtifacts modelArtifacts)
Information about the Amazon S3 location that is configured for storing model artifacts.
modelArtifacts - Information about the Amazon S3 location that is configured for storing model artifacts.default DescribeTrainingJobResponse.Builder modelArtifacts(Consumer<ModelArtifacts.Builder> modelArtifacts)
Information about the Amazon S3 location that is configured for storing model artifacts.
This is a convenience that creates an instance of theModelArtifacts.Builder avoiding the need to
create one manually via ModelArtifacts.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to modelArtifacts(ModelArtifacts).modelArtifacts - a consumer that will call methods on ModelArtifacts.BuildermodelArtifacts(ModelArtifacts)DescribeTrainingJobResponse.Builder trainingJobStatus(String trainingJobStatus)
The status of the training job.
Amazon SageMaker provides the following training job statuses:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the
FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus.
trainingJobStatus - The status of the training job.
Amazon SageMaker provides the following training job statuses:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the
FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus.
TrainingJobStatus,
TrainingJobStatusDescribeTrainingJobResponse.Builder trainingJobStatus(TrainingJobStatus trainingJobStatus)
The status of the training job.
Amazon SageMaker provides the following training job statuses:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the
FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus.
trainingJobStatus - The status of the training job.
Amazon SageMaker provides the following training job statuses:
InProgress - The training is in progress.
Completed - The training job has completed.
Failed - The training job has failed. To see the reason for the failure, see the
FailureReason field in the response to a DescribeTrainingJobResponse call.
Stopping - The training job is stopping.
Stopped - The training job has stopped.
For more detailed information, see SecondaryStatus.
TrainingJobStatus,
TrainingJobStatusDescribeTrainingJobResponse.Builder secondaryStatus(String secondaryStatus)
Provides detailed information about the state of the training job. For detailed information on the secondary
status of the training job, see StatusMessage under SecondaryStatusTransition.
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
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 - The training job has completed.
Failed - The training job has failed. The reason for the failure is returned in the
FailureReason field of DescribeTrainingJobResponse.
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
MaxWaitTmeExceeded - The job stopped because it exceeded the maximum allowed wait time.
Stopped - The training job has stopped.
Stopping - Stopping the training job.
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
secondaryStatus - Provides detailed information about the state of the training job. For detailed information on the
secondary status of the training job, see StatusMessage under
SecondaryStatusTransition.
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
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 - The training job has completed.
Failed - The training job has failed. The reason for the failure is returned in the
FailureReason field of DescribeTrainingJobResponse.
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
MaxWaitTmeExceeded - The job stopped because it exceeded the maximum allowed wait time.
Stopped - The training job has stopped.
Stopping - Stopping the training job.
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
SecondaryStatus,
SecondaryStatusDescribeTrainingJobResponse.Builder secondaryStatus(SecondaryStatus secondaryStatus)
Provides detailed information about the state of the training job. For detailed information on the secondary
status of the training job, see StatusMessage under SecondaryStatusTransition.
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
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 - The training job has completed.
Failed - The training job has failed. The reason for the failure is returned in the
FailureReason field of DescribeTrainingJobResponse.
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
MaxWaitTmeExceeded - The job stopped because it exceeded the maximum allowed wait time.
Stopped - The training job has stopped.
Stopping - Stopping the training job.
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
secondaryStatus - Provides detailed information about the state of the training job. For detailed information on the
secondary status of the training job, see StatusMessage under
SecondaryStatusTransition.
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
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 - The training job has completed.
Failed - The training job has failed. The reason for the failure is returned in the
FailureReason field of DescribeTrainingJobResponse.
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
MaxWaitTmeExceeded - The job stopped because it exceeded the maximum allowed wait time.
Stopped - The training job has stopped.
Stopping - Stopping the training job.
Valid values for SecondaryStatus are subject to change.
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
SecondaryStatus,
SecondaryStatusDescribeTrainingJobResponse.Builder failureReason(String failureReason)
If the training job failed, the reason it failed.
failureReason - If the training job failed, the reason it failed.DescribeTrainingJobResponse.Builder hyperParameters(Map<String,String> hyperParameters)
Algorithm-specific parameters.
hyperParameters - Algorithm-specific parameters.DescribeTrainingJobResponse.Builder algorithmSpecification(AlgorithmSpecification algorithmSpecification)
Information about the algorithm used for training, and algorithm metadata.
algorithmSpecification - Information about the algorithm used for training, and algorithm metadata.default DescribeTrainingJobResponse.Builder algorithmSpecification(Consumer<AlgorithmSpecification.Builder> algorithmSpecification)
Information about the algorithm used for training, and algorithm metadata.
This is a convenience that creates an instance of theAlgorithmSpecification.Builder avoiding the
need to create one manually via AlgorithmSpecification.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and
its result is passed to algorithmSpecification(AlgorithmSpecification).algorithmSpecification - a consumer that will call methods on AlgorithmSpecification.BuilderalgorithmSpecification(AlgorithmSpecification)DescribeTrainingJobResponse.Builder roleArn(String roleArn)
The AWS Identity and Access Management (IAM) role configured for the training job.
roleArn - The AWS Identity and Access Management (IAM) role configured for the training job.DescribeTrainingJobResponse.Builder inputDataConfig(Collection<Channel> inputDataConfig)
An array of Channel objects that describes each data input channel.
inputDataConfig - An array of Channel objects that describes each data input channel.DescribeTrainingJobResponse.Builder inputDataConfig(Channel... inputDataConfig)
An array of Channel objects that describes each data input channel.
inputDataConfig - An array of Channel objects that describes each data input channel.DescribeTrainingJobResponse.Builder inputDataConfig(Consumer<Channel.Builder>... inputDataConfig)
An array of Channel objects that describes each data input channel.
List.Builder avoiding the need to
create one manually via List#builder() .
When the Consumer completes, List.Builder#build() is called immediately and its
result is passed to #inputDataConfig(List) .inputDataConfig - a consumer that will call methods on List.Builder #inputDataConfig(List) DescribeTrainingJobResponse.Builder outputDataConfig(OutputDataConfig outputDataConfig)
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
outputDataConfig - The S3 path where model artifacts that you configured when creating the job are stored. Amazon
SageMaker creates subfolders for model artifacts.default DescribeTrainingJobResponse.Builder outputDataConfig(Consumer<OutputDataConfig.Builder> outputDataConfig)
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
This is a convenience that creates an instance of theOutputDataConfig.Builder avoiding the need to
create one manually via OutputDataConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to outputDataConfig(OutputDataConfig).outputDataConfig - a consumer that will call methods on OutputDataConfig.BuilderoutputDataConfig(OutputDataConfig)DescribeTrainingJobResponse.Builder resourceConfig(ResourceConfig resourceConfig)
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
resourceConfig - Resources, including ML compute instances and ML storage volumes, that are configured for model
training.default DescribeTrainingJobResponse.Builder resourceConfig(Consumer<ResourceConfig.Builder> resourceConfig)
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
This is a convenience that creates an instance of theResourceConfig.Builder avoiding the need to
create one manually via ResourceConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to resourceConfig(ResourceConfig).resourceConfig - a consumer that will call methods on ResourceConfig.BuilderresourceConfig(ResourceConfig)DescribeTrainingJobResponse.Builder vpcConfig(VpcConfig vpcConfig)
A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
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.default DescribeTrainingJobResponse.Builder vpcConfig(Consumer<VpcConfig.Builder> 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.
This is a convenience that creates an instance of theVpcConfig.Builder avoiding the need to create
one manually via VpcConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its result
is passed to vpcConfig(VpcConfig).vpcConfig - a consumer that will call methods on VpcConfig.BuildervpcConfig(VpcConfig)DescribeTrainingJobResponse.Builder stoppingCondition(StoppingCondition stoppingCondition)
Specifies a limit to how long a model training job can run. It also specifies the maximum time to wait for a spot instance. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job
termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the
results of training are not lost.
stoppingCondition - Specifies a limit to how long a model training job can run. It also specifies the maximum time to wait
for a spot instance. When the job reaches the time limit, Amazon SageMaker ends the training job. Use
this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job
termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so
the results of training are not lost.
default DescribeTrainingJobResponse.Builder stoppingCondition(Consumer<StoppingCondition.Builder> stoppingCondition)
Specifies a limit to how long a model training job can run. It also specifies the maximum time to wait for a spot instance. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job
termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the
results of training are not lost.
StoppingCondition.Builder avoiding the need to
create one manually via StoppingCondition.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to stoppingCondition(StoppingCondition).stoppingCondition - a consumer that will call methods on StoppingCondition.BuilderstoppingCondition(StoppingCondition)DescribeTrainingJobResponse.Builder creationTime(Instant creationTime)
A timestamp that indicates when the training job was created.
creationTime - A timestamp that indicates when the training job was created.DescribeTrainingJobResponse.Builder trainingStartTime(Instant trainingStartTime)
Indicates the time when the training job starts on training instances. You are billed for the time interval
between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be
later than this time. The difference is due to the time it takes to download the training data and to the
size of the training container.
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.Builder trainingEndTime(Instant trainingEndTime)
Indicates the time when the training job ends on training instances. You are billed for the time interval
between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this
is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker
detects a job failure.
trainingEndTime - Indicates the time when the training job ends on training instances. You are billed for the time
interval between the value of TrainingStartTime and this time. For successful jobs and
stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time
when Amazon SageMaker detects a job failure.DescribeTrainingJobResponse.Builder lastModifiedTime(Instant lastModifiedTime)
A timestamp that indicates when the status of the training job was last modified.
lastModifiedTime - A timestamp that indicates when the status of the training job was last modified.DescribeTrainingJobResponse.Builder secondaryStatusTransitions(Collection<SecondaryStatusTransition> secondaryStatusTransitions)
A history of all of the secondary statuses that the training job has transitioned through.
secondaryStatusTransitions - A history of all of the secondary statuses that the training job has transitioned through.DescribeTrainingJobResponse.Builder secondaryStatusTransitions(SecondaryStatusTransition... secondaryStatusTransitions)
A history of all of the secondary statuses that the training job has transitioned through.
secondaryStatusTransitions - A history of all of the secondary statuses that the training job has transitioned through.DescribeTrainingJobResponse.Builder secondaryStatusTransitions(Consumer<SecondaryStatusTransition.Builder>... secondaryStatusTransitions)
A history of all of the secondary statuses that the training job has transitioned through.
This is a convenience that creates an instance of theList.Builder
avoiding the need to create one manually via List#builder() .
When the Consumer completes, List.Builder#build() is called
immediately and its result is passed to #secondaryStatusTransitions(List) .secondaryStatusTransitions - a consumer that will call methods on List.Builder #secondaryStatusTransitions(List) DescribeTrainingJobResponse.Builder finalMetricDataList(Collection<MetricData> finalMetricDataList)
A collection of MetricData objects that specify the names, values, and dates and times that the
training algorithm emitted to Amazon CloudWatch.
finalMetricDataList - A collection of MetricData objects that specify the names, values, and dates and times
that the training algorithm emitted to Amazon CloudWatch.DescribeTrainingJobResponse.Builder finalMetricDataList(MetricData... finalMetricDataList)
A collection of MetricData objects that specify the names, values, and dates and times that the
training algorithm emitted to Amazon CloudWatch.
finalMetricDataList - A collection of MetricData objects that specify the names, values, and dates and times
that the training algorithm emitted to Amazon CloudWatch.DescribeTrainingJobResponse.Builder finalMetricDataList(Consumer<MetricData.Builder>... finalMetricDataList)
A collection of MetricData objects that specify the names, values, and dates and times that the
training algorithm emitted to Amazon CloudWatch.
List.Builder avoiding the need to
create one manually via List#builder() .
When the Consumer completes, List.Builder#build() is called immediately and its
result is passed to #finalMetricDataList(List) .finalMetricDataList - a consumer that will call methods on List.Builder #finalMetricDataList(List) DescribeTrainingJobResponse.Builder enableNetworkIsolation(Boolean enableNetworkIsolation)
If you want to allow inbound or outbound network calls, except for calls between peers within a training
cluster for distributed training, choose True. If you enable network isolation for training jobs
that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts
through the specified VPC, but the training container does not have network access.
The Semantic Segmentation built-in algorithm does not support network isolation.
enableNetworkIsolation - If you want to allow inbound or outbound network calls, except for calls between peers within a
training cluster for distributed training, choose True. If you enable network isolation
for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer
data and model artifacts through the specified VPC, but the training container does not have network
access. The Semantic Segmentation built-in algorithm does not support network isolation.
DescribeTrainingJobResponse.Builder enableInterContainerTrafficEncryption(Boolean enableInterContainerTrafficEncryption)
To encrypt all communications between ML compute instances in distributed training, choose True.
Encryption provides greater security for distributed training, but training might take longer. How long it
takes depends on the amount of communication between compute instances, especially if you use a deep learning
algorithms in distributed training.
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.Builder enableManagedSpotTraining(Boolean enableManagedSpotTraining)
A Boolean indicating whether managed spot training is enabled (True) or not (False
).
enableManagedSpotTraining - A Boolean indicating whether managed spot training is enabled (True) or not (
False).DescribeTrainingJobResponse.Builder checkpointConfig(CheckpointConfig checkpointConfig)
checkpointConfig - The new value for the CheckpointConfig property for this object.default DescribeTrainingJobResponse.Builder checkpointConfig(Consumer<CheckpointConfig.Builder> checkpointConfig)
CheckpointConfig.Builder avoiding the need to
create one manually via CheckpointConfig.builder().
When the Consumer completes, SdkBuilder.build() is called immediately and its
result is passed to checkpointConfig(CheckpointConfig).checkpointConfig - a consumer that will call methods on CheckpointConfig.BuildercheckpointConfig(CheckpointConfig)DescribeTrainingJobResponse.Builder trainingTimeInSeconds(Integer trainingTimeInSeconds)
The training time in seconds.
trainingTimeInSeconds - The training time in seconds.DescribeTrainingJobResponse.Builder billableTimeInSeconds(Integer billableTimeInSeconds)
The billable time in seconds.
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 - The billable time in seconds.
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%.
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