Paws::SageMaker::DescribeTrainingJobResponse
Information about the algorithm used for training, and algorithm metadata.
A timestamp that indicates when the training job was created.
To encrypt all communications between ML compute instances in distributed training, specify True. Encryption provides greater security for distributed training, but training take longer because of the additional communications between ML compute instances.
True
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.
If the training job failed, the reason it failed.
A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
MetricData
Algorithm-specific parameters.
An array of Channel objects that describes each data input channel.
Channel
The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
A timestamp that indicates when the status of the training job was last modified.
Information about the Amazon S3 location that is configured for storing model artifacts.
The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.
Resources, including ML compute instances and ML storage volumes, that are configured for model training.
The AWS Identity and Access Management (IAM) role configured for the training job.
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.
StatusMessage
Amazon SageMaker provides primary statuses and secondary statuses that apply to each of them:
Starting - Starting the training job.
Starting
Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.
Downloading
File
Training - Training is in progress.
Training
Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.
Uploading
Completed - The training job has completed.
Completed
Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.
Failed
FailureReason
DescribeTrainingJobResponse
MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.
MaxRuntimeExceeded
Stopped - The training job has stopped.
Stopped
Stopping - Stopping the training job.
Stopping
Valid values for SecondaryStatus are subject to change.
SecondaryStatus
We no longer support the following secondary statuses:
LaunchingMLInstances
PreparingTrainingStack
DownloadingTrainingImage
Valid values are: "Starting", "LaunchingMLInstances", "PreparingTrainingStack", "Downloading", "DownloadingTrainingImage", "Training", "Uploading", "Stopping", "Stopped", "MaxRuntimeExceeded", "Completed", "Failed" =head2 SecondaryStatusTransitions => ArrayRef[Paws::SageMaker::SecondaryStatusTransition]
"Starting"
"LaunchingMLInstances"
"PreparingTrainingStack"
"Downloading"
"DownloadingTrainingImage"
"Training"
"Uploading"
"Stopping"
"Stopped"
"MaxRuntimeExceeded"
"Completed"
"Failed"
A history of all of the secondary statuses that the training job has transitioned through.
The condition under which to stop the training job.
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.
TrainingStartTime
The Amazon Resource Name (ARN) of the training job.
Name of the model training job.
The status of the training job.
Amazon SageMaker provides the following training job statuses:
InProgress - The training is in progress.
InProgress
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.
For more detailed information, see SecondaryStatus.
Valid values are: "InProgress", "Completed", "Failed", "Stopping", "Stopped" =head2 TrainingStartTime => Str
"InProgress"
Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
TrainingEndTime
The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
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 (http://docs.aws.amazon.com/sagemaker/latest/dg/train-vpc.html).
To install Paws::SDK::Config, copy and paste the appropriate command in to your terminal.
cpanm
cpanm Paws::SDK::Config
CPAN shell
perl -MCPAN -e shell install Paws::SDK::Config
For more information on module installation, please visit the detailed CPAN module installation guide.