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Getting Started with Jupyter on AWS

Features

Amazon CodeWhisperer  is an AI coding companion that generates real-time, single-line or full-function code suggestions. You can install and use CodeWhisperer for free in JupyterLab or Amazon SageMaker Studio.

To get started, use the following resources:

With the notebooks scheduling tool, you can select a notebook and automate it as a job that can run in a production environment via a simple yet powerful UI. You can use this capability in SageMaker Studio or Studio Lab, and you can install the Jupyter open source extension wherever you run Jupyter. This simple user experience enables you to move from interactive exploration to production jobs in a matter of seconds.

To get started, use the following resources:

The easiest way to get started with Jupyter on AWS is with  Amazon SageMaker Studio Lab . With only an email address and a mobile phone number (no AWS account required), you can use JupyterLab on AWS with free persistent storage and compute (CPU and GPU). Amazon SageMaker Studio Lab has Git and GitHub integration, and supports open-source Jupyter extensions. It is designed for individuals who want to use Jupyter for learning and introductory work.

To get started with SageMaker Studio Lab, use the following resources:

Amazon SageMaker Studio  provides a fully-managed Jupyter experience with the security, reliability, and scalability needed for production use at scale. Built on JupyterLab, Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x.

To get started with SageMaker Studio, use the following resources: