Log groups and streams that Amazon SageMaker AI sends to Amazon CloudWatch Logs - Amazon SageMaker AI

Log groups and streams that Amazon SageMaker AI sends to Amazon CloudWatch Logs

To help you debug your compilation jobs, processing jobs, training jobs, endpoints, transform jobs, notebook instances, and notebook instance lifecycle configurations, anything an algorithm container, a model container, or a notebook instance lifecycle configuration sends to stdout or stderr is also sent to Amazon CloudWatch Logs. In addition to debugging, you can use these for progress analysis.

By default, log data is stored in CloudWatch Logs indefinitely. However, you can configure how long to store log data in a log group. For information, see Change Log Data Retention in CloudWatch Logs in the Amazon CloudWatch Logs User Guide.

Logs

The following table lists all of the logs provided by Amazon SageMaker AI.

Logs

Log Group Name Log Stream Name
/aws/sagemaker/CompilationJobs

[compilation-job-name]
/aws/sagemaker/Endpoints/[EndpointName]

[production-variant-name]/[instance-id]

(For Asynchronous Inference endpoints) [production-variant-name]/[instance-id]/data-log

(For Inference Pipelines) [production-variant-name]/[instance-id]/[container-name provided in SageMaker AI model]
/aws/sagemaker/groundtruth/WorkerActivity

aws/sagemaker/groundtruth/worker-activity/[requester-AWS-Id]-[region]/[timestamp]
/aws/sagemaker/InferenceRecommendationsJobs

[inference-recommendations-job-name]/execution

[inference-recommendations-job-name]/CompilationJob/[compilation-job-name]

[inference-recommendations-job-name]/Endpoint/[endpoint-name]
/aws/sagemaker/LabelingJobs

[labeling-job-name]
/aws/sagemaker/NotebookInstances

[notebook-instance-name]/[LifecycleConfigHook]

[notebook-instance-name]/jupyter.log
/aws/sagemaker/ProcessingJobs

[processing-job-name]/[hostname]-[epoch_timestamp]
/aws/sagemaker/studio

[domain-id]/[user-profile-name]/[app-type]/[app-name]

[domain-id]/domain-shared/rstudioserverpro/default
/aws/sagemaker/TrainingJobs

[training-job-name]/algo-[instance-number-in-cluster]-[epoch_timestamp]
/aws/sagemaker/TransformJobs

[transform-job-name]/[instance-id]-[epoch_timestamp]

[transform-job-name]/[instance-id]-[epoch_timestamp]/data-log

[transform-job-name]/[instance-id]-[epoch_timestamp]/[container-name provided in SageMaker AI model] (For Inference Pipelines)
Note

1. The /aws/sagemaker/NotebookInstances/[LifecycleConfigHook] log stream is created when you create a notebook instance with a lifecycle configuration. For more information, see Customization of a SageMaker notebook instance using an LCC script.

2. For Inference Pipelines, if you don't provide container names, the platform uses **container-1, container-2**, and so on, corresponding to the order provided in the SageMaker AI model.

For more information about logging events with CloudWatch logging, see What is Amazon CloudWatch Logs? in the Amazon CloudWatch User Guide.