Jupyter Lab vs Sagemaker Studio Classic

The architecture of Studio Classic and the new JupyterLab is different. The JupyterLab application runs on a single Amazon Elastic Compute Cloud (Amazon EC2) instance and uses a single Amazon Elastic Block Store (Amazon EBS) volume for storage.

The Studio Classic architecture uses JupyterServer to host the Kernel and the UI Server is hosted on a Kernel Gateway server to connect to the actual backend server. It also uses a shared FsX storage with user specific permission.

The new architecture provides a much faster experience. For more details refer the links below: https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated.html https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-jl.html

If you want to bring in custom libraries you can install them via pypl in the jupyter environment provided the Jupyter environment has access to the internet either via a NAT Gateway or Internet Gateway. If not you can upload custom libraries in a S3 bucket in your account and install from there.