# Monitoring

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This page was generated from content adapted from the [AWS Developer Guide](https://github.com/awsdocs/amazon-sagemaker-developer-guide.git)
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## Monitoring with CloudWatch

* **Note**\
  Amazon CloudWatch supports [high-resolution custom metrics](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/publishingMetrics.html) and its finest resolution is 1 second. However, the finer the resolution, the shorter the lifespan of the CloudWatch metrics. For the 1-second frequency resolution, the CloudWatch metrics are available for 3 hours. For more information about the resolution and the lifespan of the CloudWatch metrics, see [GetMetricStatistics](https://docs.aws.amazon.com/AmazonCloudWatch/latest/APIReference/API_GetMetricStatistics.html) in the *Amazon CloudWatch API Reference*.
* **Tip**\
  If you want to profile your training job with a finer resolution down to 100-millisecond (0.1 second) granularity and store the training metrics indefinitely in Amazon S3 for custom analysis at any time, consider using [Amazon SageMaker Debugger](https://docs.aws.amazon.com/sagemaker/latest/dg/train-debugger.html). SageMaker Debugger provides built-in rules to automatically detect common training issues; it detects hardware resource utilization issues (such as CPU, GPU, and I/O bottlenecks) and non-converging model issues (such as overfit, vanishing gradients, and exploding tensors). SageMaker Debugger also provides visualizations through Studio and its profiling report. To explore the Debugger visualizations, see [SageMaker Debugger Insights Dashboard Walkthrough](https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-on-studio-insights-walkthrough.htm), [Debugger Profiling Report Walkthrough](https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-profiling-report.html#debugger-profiling-report-walkthrough), and [Analyze Data Using the SMDebug Client Library](https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-analyze-data.html).

## Logging with CloudWatch

* **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 [Customize a Notebook Instance Using a Lifecycle Configuration Script](https://github.com/kevinslin/aws-reference-notes/blob/main/services/amazon_sage_maker/notebook-lifecycle-config.md).\
  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 model.

## Automating with EventBridge

* **Important**\
  The following examples may not work for all endpoints. For a list of features that may exclude your endpoint, see the [Exclusions](https://github.com/kevinslin/aws-reference-notes/blob/main/services/amazon_sage_maker/deployment-guardrails-exclusions.md) page.