You can read more about it on its Github page. If you save your work, your changes will be committed back to the Domino project so your colleagues can use them. Note that this is running through Domino’s execution layer: we’ve launched it on a hardware tier that you can choose, and we’ve setup all the necessary network configuration to drop you into the environment. You’ll be dropped into a JupyterLab environment, where you can play with all its functionality.
#JUPYTER VS JUPYTERLAB HOW TO#
How to use JupyterLab in Dominoġ) From the “notebook” dropdown menu, choose “JupyterLab"Ģ) When the green “open session” button appears, click it Today, we’re excited to announce support for JupyterLab, along with a flexible plugin framework that lets data scientists add their own tools to run on the Domino platform. Most of the data scientists who use Domino work with Python, R, Jupyter, and RStudio, though we support a variety of others. Throughout those stages of work, Domino lets data scientists work on scalable self-service infrastructure while enabling best practices like reproducibility and collaboration.įrom the beginning, our philosophy has been to be an open platform that supports the latest open source tools. Introductionĭomino is a data science platform that supports the entire data science lifecycle, from exploratory analysis through experimentation and all the way to deployment. You can now run JupyterLab in Domino, using a new Domino feature that lets data scientists specify any web-based tools they want to run on top of the Domino platform.
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