JupyterLab is central to AXXE-L. It is via JupyterLab that users interact with AXXE-L when submitting jobs, so we thought we’d provide a JupyterLab quick starter guide for our AXXE-L users.
If you’ve never heard of Project Jupyter, it’s run by a team which develops Open Source software that enables interactive computing in most programming languages. The team initially gained popularity with its Open Source project called Jupyter Notebook. And their next project was JupyterLab.
JupyterLab is a web-based, interactive development environment for Jupyter Notebook, code, and data. It is flexible, extensible, and modular, and allows the use of Terminal, Text Editor, Jupyter Notebook, and file viewers, and can open or edit popular file formats such as JSON, CSV, Markdown, and others.
With the help of developers who create extensions for the community, JupyterLab is continuously evolving. Developers can create their own extensions for the community or for their own commercial projects.
JupyterLab can be installed in several ways, depending on your preferences:
Installation with conda
conda install -c conda-forge jupyterlab
Installation with mamba
mamba install -c conda-forge jupyterlab
Installation with pip
pip install jupyterlab
or you can install it using Anaconda Navigator since it comes with JupyterLab, Jupyter notebook, and other applications.
To start JupyterLab, open your terminal and run
Then open your browser and access localhost:8888/lab.
If you’re using Anaconda Navigator, open the app and click JupyterLab > Launch.
You should be able to see the JupyterLab on your browser and this is what it looks like.
Note: The Bash from Notebook and Console sections are installed independently. If you wish to use this package, please check bash_kernel.
To start, you can click Notebooks > Python and it will create a Jupyter notebook file.
From there, you can execute Python code or create a set of Python instructions and run it interactively.
number = input("Enter a number")
Just like that! Amazing, isn’t it? You can do anything with JupyterLab — from studying a programming language like Python to running AI, Machine Learning, and other workflows. JupyterLab offers many extensions that can be installed to support whatever you’re working on.
Some of my favorite extensions are JupyterLab-DrawIO, JupyterLab-Spreadsheet, IPywidgets, and Jupyter Matplotlib. These range from productivity-enhancing extensions to tools for a data scientist. The community is massive and all of the members support each other. You can check out the Jupyter community here.
That’s all for the quick starter guide on JupyterLab. Our next blog will be on how to deploy JupyterLab at the enterprise level.
<About the author>
Ray Marc Marcellones has been an HPC Engineer at XTREME-D Inc. for more than three years. He is engaged in the development of both a back end and a front end for AXXE-L Web and Services.