> For the complete documentation index, see [llms.txt](https://docs.canso.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.canso.ai/guides/dry_runs.md).

# Dry Runs for Batch ML Features

A user can do dry run for any registered feature even before doing the actual deployment of the given feature.

* It helps end user to do deployment in production more confidently
* If any issues ocuurs while doing the dry run can be resolved well before doing the actual deployment.

## Introduction

Dry run is a way to test the feature before it gets deployed to production. It helps end users verify the feature logic they're implementing and reduces development and testing time. A user can do a dry run for any registered feature even before doing the actual deployment of the given feature.

* It helps end users deploy features in production confidently
* If there's any issue with the dry run output then it can be resolved well before doing the actual deployment.

## How to do a dry run?

* Same way, user creates a feature and does feature.deploy(), user will call feature.dry\_run() method.
* User will have to pass the start date and end date for the Dry run. Internally, an Airflow DAG will be scheduled for the given duration.
* Users will also have to specify the MAX\_DRY\_RUN\_DURATION\_DAYS.

## Important Notes

* Users are not allowed to do online ingestion in a dry run. Only offline materialization is in scope.

## Examples

This \[example]\(#TODO need to add url here) demonstrates how to perform a dry run for a Raw Feature. Once the `dry_run` is executed a new DAG with the name "test\_crf\_sha\_testing\_rows\_4" is generated, which allows users to inspect the job, reference the materialised values, perform any quality checks they want. Upon completion of quality checks, the same feature can be deployed.