Scheduler, Batch Mode, and Test Conversion

By Sarthak Rajani
November 13, 2024 | 3 min read

Welcome to RAP! Here is an introductory guide to Scheduler, Batch Mode, and Test Conversion.

Scheduler allows you to run your notebook(s) in the background at periodic intervals without any user intervention. This is done using a CRON job and is useful when you intend to run an analysis report at fixed regular intervals (for example daily, weekly, or monthly).

Batch Mode helps in running your notebook without the need to stay active on the platform. As soon as you click the “Run in Batch Mode” button, the notebook will start its execution in the background. This is useful for long running jobs so that you can log off and return later to view/download the results.

Test Conversion ensures that the notebook does not consist of any syntax errors and is ready to be executed in the background. We highly recommend using this before scheduling your notebook or running it in batch mode. Do note, Test Conversion does not check for logic errors.

Notebook Toolbar displaying above listed features

Scheduler

1. When you click on the Scheduler icon, the following dialogue box will appear.

2. You can simply use the list options to select the frequency of your run between day(s) or month(s) and add in a preferred time as well.

NOTE: The time stated is in UTC. Please convert your preferred scheduling time to UTC and use that value.

3. Or you can use an AWS CRON expression which gives you more feasibility to schedule your runs. RAP uses a CRON format that consists of six required fields. Each one is separated from the others by a space in between, with no leading or trailing spaces:

You can click on “Show Cron expression requirements” or visit this link to learn more about it.

4. Once you are done, you can toggle the “Inactive” button to “Active” and click on SAVE.

Sarthak Rajani, Data Scientist

After transitioning from engineering to fintech, Sarthak earned a master’s in quantitative finance from the University of Maryland, College Park. He now excels as a data scientist on the RAP team, bringing profound expertise in data science to drive innovative solutions and strategic insights, along with substantial skills in product specialization and client support.