In this post, we will explore using Jupyter notebook as a development environment for DevOps for AWS. We will use an example of developing a mechanism to save 70% of the cost notebook instances in SageMaker, using automatic stop and start at the end and beginning of every working day. Since the Jupyter notebooks are used interactively during the working hours, stopping them and stopping to pay for them during the nights and the weekends can save about 70% of the costs. We can ask our data scientists or DevOps teams to shut them manually when they leave the office or don't need them, but they won't. We will use the following automation flow:

• A CloudWatch Scheduled Event called OnDuty that is triggered every working morning
• A Lambda function that is checking for notebook instances with a tag InDuty=Yes and starting them
• A CloudWatch Scheduled Event called OffDuty that is triggered every working evening
• The same Lambda function stoping the InDuty instances

We will be using a Jupyter notebook to develop the above Lambda function and the Terraform scripts to create the above environment. In AllCloud we have a lot of DevOps code and a lot of infrastructures to manage, and we found it hard to collaborate with the teams, team members and our customers. Since Python is also the most common language of our development, using Jupyter notebook as a development environment allow us to share internally and externally easier.