This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks. A public GitHub repo provides hands-on examples for each of the presented approaches.
Amazon 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. Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity.
Studio notebooks are collaborative Jupyter notebooks that you can launch quickly because you don’t need to set up compute instances and file storage beforehand. When you open a notebook in Studio, you are prompted to set up your environment by choosing a SageMaker image, a kernel, an instance type, and, optionally, a lifecycle configuration script that runs on image startup.
For more details on Studio notebook

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