# Generate reports
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This guide provides step-by-step instructions to configure and manage usage reporting for your SageMaker HyperPod clusters. Follow these procedures to deploy infrastructure, generate custom reports, and remove resources when no longer needed.

## Set up usage reporting
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**Note**  
Before configuring the SageMaker HyperPod usage report infrastructure in your SageMaker HyperPod cluster, ensure you have met all prerequisites detailed in this [https://github.com/awslabs/sagemaker-hyperpod-usage-report/blob/main/README.md#prerequisites](https://github.com/awslabs/sagemaker-hyperpod-usage-report/blob/main/README.md#prerequisites).

Usage reporting in HyperPod requires:
+ Deploying SageMaker HyperPod usage report AWS resources using an CloudFormation stack
+ Installing the SageMaker HyperPod usage report Kubernetes operator via a Helm chart

You can find comprehensive installation instructions in the [SageMaker HyperPod usage report GitHub repository](https://github.com/awslabs/sagemaker-hyperpod-usage-report/blob/main/README.md). Specifically, follow the steps in the [Set up](https://github.com/awslabs/sagemaker-hyperpod-usage-report/blob/main/README.md#set-up-usage-reporting) section.

## Generate usage reports on demand
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Once the usage reporting infrastructure and Kubernetes operator are installed, job data for your SageMaker HyperPod cluster is automatically collected and stored in the S3 bucket you configured during setup. The operator continuously captures detailed usage metrics in the background, creating raw data files in the `raw` directory of your designated S3 bucket.

To generate an on-demand usage report, you can use the `run.py` script provided in the [SageMaker HyperPod usage report GitHub repository](https://github.com/awslabs/sagemaker-hyperpod-usage-report/blob/main/README.md) to extract and export usage metrics. Specifically, you can find the script and comprehensive instructions for generating a report in the [Generate Reports](https://github.com/awslabs/sagemaker-hyperpod-usage-report/blob/main/README.md#generate-reports) section.

The script allows you to:
+ Specify custom date ranges for report generation
+ Choose between detailed and summary report types
+ Export reports in CSV or PDF formats
+ Direct reports to a specific S3 location

## Clean up usage reporting resources
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When you no longer need your SageMaker HyperPod usage reporting infrastructure, follow the steps in [Clean Up Resources](https://github.com/awslabs/sagemaker-hyperpod-usage-report/blob/main/README.md#clean-up-resources) to clean up the Kubernetes operator and AWS resources (in that order). Proper resource deletion helps prevent unnecessary costs.