# CloudWatch Logs for Amazon SageMaker AI
<a name="logging-cloudwatch"></a>

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](https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/Working-with-log-groups-and-streams.html#SettingLogRetention) in the *Amazon CloudWatch Logs User Guide*.

**Logs**

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

**Logs**

[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/sagemaker/latest/dg/logging-cloudwatch.html)

**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](notebook-lifecycle-config.md).  
2. For Inference Pipelines, if you don't provide container names, the platform uses \$1\$1container-1, container-2\$1\$1, 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?](https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/WhatIsCloudWatchLogs.html) in the *Amazon CloudWatch User Guide*.