Jupyter on AWS Features

Amazon CodeWhisperer is an AI coding companion that generates real-time, single-line or full-function code suggestions. With CodeWhisperer, you can write a comment in natural language that outlines a specific task in English, such as “Create a pandas dataframe using a CSV file” Based on this information, CodeWhisperer recommends one or more code snippets directly in the notebook that can accomplish the task. You can quickly and easily accept the top suggestion, view more suggestions, or continue writing your own code. Jupyter users can install and use CodeWhisperer extension for free in JupyterLab and Amazon SageMaker Studio.

Jupyter users can select a notebook and automate it as a job that can run in a production environment via a simple yet powerful user interface. Once a notebook is selected, the tool takes a snapshot of the entire notebook, packages its dependencies in a container, builds the infrastructure, runs the notebook as an automated job on a schedule set by the user, and deprovisions the infrastructure upon job completion, reducing the time it takes to move a notebook to production from weeks to hours.

Amazon SageMaker Studio Lab is a free machine learning (ML) development environment that provides the compute, storage (up to 15 GB), and security—all at no cost—for anyone to learn and experiment with Jupyter for ML. All you need to get started is a valid email address—you don’t need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later.

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. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, collaborate seamlessly within your organization, and deploy models to production without leaving SageMaker Studio. Amazon SageMaker Studio notebooks are collaborative Jupyter notebooks that integrate with purpose-built ML tools in SageMaker and other AWS services for your complete ML development, from preparing data at petabyte scale using Spark on Amazon EMR, to training and debugging models, tracking experiments, deploying and monitoring models and managing pipelines. Easily dial compute resources up or down without interrupting your work. Share notebooks easily with your team using a sharable link or even coedit the same single notebook in real time.

You can also use the standalone, fully managed Jupyter notebook instances on Amazon SageMaker. Choose from the broadest selection of compute resources available in the cloud, including GPUs for accelerated computing, and work with the latest versions of open-source software that you trust.