In the past, customisations to Amazon SageMaker Studio were possible but relied on manual adjustments each and every time! Every time an app was deleted or recreated, you had to manually reapply the custom changes, making these adjustments tedious for any Developer or Data Scientist involved!
AWS have recently announced the end to this repetitive cycle with the introduction of Lifecycle Configurations. These configurations are essentially shell scripts that are triggered by Studio Lifecycle events (such as starting a new Studio notebook). These scripts can be used to automate customisations for specific studio environments, such as preloading datasets, setting up source code repositories or installing JupyterLab extensions (all very useful and time saving features)!
Starting the App within the Notebook section.
Three Top Tips
To get users started with the features of Lifecycle Configurations, AWS have featured three common customisations use studies. These are:
• Installing custom packages
• Configuring auto-shutdown of inactive notebook apps
• Setting up Git configurations
These configuration tips provide a way to automatically and repeatedly apply custom changes with ease.
Amazon SageMaker x Firemind
As an AWS Advanced Partner specialising in ML and Data centric solutions, we couldn’t be happier with the recent addition of Lifecycle Configurations. Any tool that removes Data Scientist barriers and reduces the time needed on repeatable tasks is always a blessing.
We’re particularly excited by the auto-shutdown features of inactive kernels, ensuring we can save costs after long periods of inactivity.
As always, we’re keeping a close eye on any and all developments within Amazon SageMaker Studio, ensuring our team is making the most of all time saving features.
Would you like to know more about how Firemind can work on your cloud project using Amazon SageMaker?
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