Studio KernelGateway apps and notebooks kernels Use the Studio Amazon Elastic File System (Amazon EFS) volume to persist Conda environments.Use Studio notebook lifecycle configurations.Use a custom Studio KernelGateway app image.This post considers the following approaches for customizing Studio environments by managing packages and creating Python virtual environments in Studio notebooks:
To ensure the best fit for your development process and phases, access to specific or latest ML frameworks, or to fulfil data access and governance requirements, you can customize the pre-built notebook environments or create new environments using your own images and kernels. Each image can host one or multiple kernels, which can be different virtual environments for development. Studio comes with pre-built images that include the latest Amazon SageMaker Python SDK and, depending on the image type, other specific packages and resources, such as Spark, MXNet, or PyTorch framework libraries, and their required dependencies. Studio notebooks are designed to support you in all phases of your ML development, for example, ideation, experimentation, and operationalization of an ML workflow.
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.įor more details on Studio notebook concepts and other aspects of the architecture, refer to Dive deep into Amazon SageMaker Studio Notebooks architecture. 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. Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity. A public GitHub repo provides hands-on examples for each of the presented approaches.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. This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks.
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