![]() The following sample script installs pyarrow using the pip package manager. Start the Studio app with the specified lifecycle configuration.Associate the lifecycle configuration to a domain or user profile.Create a lifecycle configuration entity via the AWS Command Line Interface (AWS CLI).Convert the script to a base64 encoded string.The following is the typical workflow for using lifecycle configuration in your apps: ![]() Let’s see how to use lifecycle configuration to automate the installation of this dependency in the kernel. Say that you need to install pyarrow in your notebook environment so that you can work with a Parquet-formatted training dataset for your ML model. Lifecycle configuration allows you to automate this process without the need to build a custom Studio image. One common use case for lifecycle configuration is to install custom libraries so they’re available right away whenever you start a new kernel app. Install custom packages on base kernel images Configuring auto-shutdown of inactive notebook appsįor more examples, visit the SageMaker Studio Lifecycle Configuration Samples repository on GitHub.In this post, we show you how to use lifecycle configurations for three common customization use cases: Lifecycle configuration provides a way to automatically and repeatably apply your customizations. Previously, customizations to Studio environments were possible, but you needed to reapply them manually every time apps were deleted or recreated. You can use these shell scripts to automate customization for your Studio environments, such as installing JupyterLab extensions, preloading datasets, and setting up source code repositories. Lifecycle configurations are shell scripts triggered by Studio lifecycle events, such as starting a new Studio notebook. We’re excited to announce Lifecycle Configuration for Studio, a new capability that enables developers to automate customization for your Studio development environments. You can write code, track experiments, visualize data, and perform debugging and monitoring within a single, integrated visual interface. It provides all the tools you need to take your models from experimentation to production while boosting your productivity. This blog post was last reviewed and updated February, 2022 to comply with the latest version of autoshutdown plugin.Īmazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models.
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