MLCOST-16: Stop resources when not in use
Stop resources that are not in use to reduce cost. For example, hosted Jupyter environments used to explore small samples of data, can be stopped when not actively in use. Where practical, commit the work, stop them, and restart when needed. The same approach can be used to stop the computing and the data storage services.
Implementation plan
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Use CloudWatch Billing Alarms - You can monitor your estimated AWS charges by using Amazon CloudWatch. When you enable the monitoring of estimated charges for your AWS account, the estimated charges are calculated and sent several times daily to CloudWatch as metric data. Use this feature to receive notifications when your resource charge exceeds a threshold amount.
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Use SageMaker Lifecycle Configuration - A lifecycle configuration provides shell scripts that run when you create the notebook instance or whenever you start one. When you create a notebook instance, you can create a new lifecycle configuration and its scripts or apply ones that you already have. Use a lifecycle configuration script to access AWS services from your notebook. These scripts enable checking notebook instance activities and shut them down if idle.
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Use Amazon SageMaker Studio auto shutdown- Amazon SageMaker Studio provides a unified, web-based visual interface for performing all ML development steps, making data science teams more productive. Idle SageMaker Studio notebooks can be detected and stopped using an auto-shutdown JupyterLab extension that can be installed manually or automatically
. You can shut down individual resources, including notebooks, terminals, kernels, applications, and instances.
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