SageMaker JupyterLab

Create a JupyterLab space within Amazon SageMaker Studio to launch the JupyterLab application. A JupyterLab space is a private or shared space within Studio that manages the storage and compute resources needed to run the JupyterLab application. The JupyterLab application is a web-based interactive development environment (IDE) for notebooks, code, and data. Use the JupyterLab application's flexible and extensive interface to configure and arrange machine learning (ML) workflows.

By default, the JupyterLab application comes with the SageMaker Distribution image. The distribution image has popular packages, such as the following:

You can use shared spaces to collaborate on your Jupyter notebooks with other users in real time. For more information about shared spaces, see Collaborate with shared spaces.

Within the JupyterLab application, you can use Amazon CodeWhisperer, a generative AI powered code companion to generate, debug, and explain your code.

Build unified analytics and ML workflows in same Jupyter notebook. Run interactive Spark jobs on Amazon EMR and Amazon Glue serverless infrastructure, right from your notebook. Monitor and debug jobs faster using the inline Spark UI. In a few steps, you can automate your data prep by scheduling the notebook as a job.

The JupyterLab application helps you work collaboratively with your peers. Use the built-in Git integration within the JupyterLab IDE to share and version code. Bring your own file storage system if you have an Amazon EFS volume.

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. You can switch faster instances or increase the Amazon EBS volume size for your needs.

The JupyterLab 4 application runs in a JupyterLab space within Studio. Studio Classic uses the JupyterLab 3 application. JupyterLab 4 provides the following benefits:

For more information about JupyterLab, see JupyterLab Documentation.