SageMaker Training Job

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An AWS SageMaker Training Job is a managed resource that runs machine learning model training on specified datasets using chosen algorithms and compute resources. It handles provisioning of infrastructure, scaling, monitoring, and storing model artifacts in Amazon S3. This allows developers and data scientists to focus on model design and data preparation while SageMaker manages the training process efficiently.

aws.sagemaker_training_job

Fields

ID	Type	Data Type	Description
_key	core	string
account_id	core	string
algorithm_specification	core	json	Information about the algorithm used for training, and algorithm metadata.
auto_ml_job_arn	core	string	The Amazon Resource Name (ARN) of an AutoML job.
billable_time_in_seconds	core	int64	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%.
checkpoint_config	core	json	Contains information about the output location for managed spot training checkpoint data.
creation_time	core	timestamp	A timestamp that indicates when the training job was created.
debug_hook_config	core	json	Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths. To learn more about how to configure the DebugHookConfig parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job.
debug_rule_configurations	core	json	Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
debug_rule_evaluation_statuses	core	json	Evaluation status of Amazon SageMaker Debugger rules for debugging on a training job.
enable_inter_container_traffic_encryption	core	bool	To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithms in distributed training.
enable_managed_spot_training	core	bool	A Boolean indicating whether managed spot training is enabled (True) or not (False).
enable_network_isolation	core	bool	If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
environment	core	hstore	The environment variables to set in the Docker container. Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.
experiment_config	core	json	Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the following APIs: CreateProcessingJob CreateTrainingJob CreateTransformJob
failure_reason	core	string	If the training job failed, the reason it failed.
final_metric_data_list	core	json	A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
hyper_parameters	core	hstore	Algorithm-specific parameters.
infra_check_config	core	json	Contains information about the infrastructure health check configuration for the training job.
input_data_config	core	json	An array of Channel objects that describes each data input channel.
labeling_job_arn	core	string	The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling job that created the transform or training job.
last_modified_time	core	timestamp	A timestamp that indicates when the status of the training job was last modified.
model_artifacts	core	json	Information about the Amazon S3 location that is configured for storing model artifacts.
output_data_config	core	json	The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.
profiler_config	core	json	Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
profiler_rule_configurations	core	json	Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
profiler_rule_evaluation_statuses	core	json	Evaluation status of Amazon SageMaker Debugger rules for profiling on a training job.
profiling_status	core	string	Profiling status of a training job.
remote_debug_config	core	json	Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
resource_config	core	json	Resources, including ML compute instances and ML storage volumes, that are configured for model training.
retry_strategy	core	json	The number of times to retry the job when the job fails due to an InternalServerError.
role_arn	core	string	The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
secondary_status	core	string	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
secondary_status_transitions	core	json	A history of all of the secondary statuses that the training job has transitioned through.
stopping_condition	core	json	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.
tags	core	hstore
tensor_board_output_config	core	json	Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.
training_end_time	core	timestamp	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.
training_job_arn	core	string	The Amazon Resource Name (ARN) of the training job.
training_job_name	core	string	Name of the model training job.
training_job_status	core	string	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.
training_start_time	core	timestamp	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.
training_time_in_seconds	core	int64	The training time in seconds.
tuning_job_arn	core	string	The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
vpc_config	core	json	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.
warm_pool_status	core	json	The status of the warm pool associated with the training job.