JupyterLab 4.0: Experience Enhanced Speed with Progressive Loading
Written on
Chapter 1: Introduction to JupyterLab 4.0
JupyterLab 4.0 has made significant strides in speed compared to earlier versions, while also retaining many cherished features. It's June, a month when the tech industry often slows down as many take vacations, yet the dedicated JupyterLab team continues their hard work.
You may already be familiar with Jupyter, an interactive notebook framework that facilitates running custom code and installing various extensions. Jupyter encompasses several platforms: Jupyter Notebook, Jupyter Desktop, and JupyterLab, which is anticipated to eventually replace Notebook as the go-to option.
Section 1.1: Installation and Use
JupyterLab is developed in Python, enabling it to execute Python code and other languages. Installation can be done through standard Python methods or by downloading a Docker image for quick setup. Once installed, you can easily launch JupyterLab in your preferred web browser.
With the concept of kernels, users can write code in a multitude of languages within their notebooks, allowing for the creation of interactive interfaces or microapplications. Supported languages include JavaScript, TypeScript, Ruby, Haskell, C#, Go, Scala, Ocaml, PHP, Perl, C, C++, and even Brainfuck.
To explore JupyterLab without installation, you can access an interactive demo online, though it’s best viewed on a laptop rather than a smartphone. Additionally, Jupyter can be integrated with IDEs like VSCode for more complex tasks, allowing you to use Notebook, Lab, and an IDE concurrently.
Chapter 2: Key Enhancements in JupyterLab 4.0
JupyterLab serves as a powerful tool for building documents that include code, visualizations, equations, and interactive features. It has gained popularity among developers, educators, and data scientists, thanks to its capability to merge various data sources, generate graphs, and draw insights from existing information.
The latest release introduces two major enhancements aimed at improving speed and efficiency. The first addresses concerns from users who found JupyterLab to be sluggish. The development team has implemented significant optimizations, most notably a progressive rendering feature that loads only the elements visible in the user's viewport.
This means that as you scroll through a document, it loads progressively rather than all at once. While this may introduce a slight delay when loading sections after scrolling, the overall experience is more efficient and streamlined.
Section 2.1: Performance Improvements
In addition to progressive rendering, CSS rules have been updated for enhanced performance. Early adopters can take advantage of two additional performance tweaks available for Chromium browsers:
- Navigate to "Settings" → "JupyterLab Shell" and switch "Hidden mode" to "contentVisibility."
- In "Settings" → "Notebook," change "Windowing mode" to "full."
Section 2.2: New Features in Text Editing
The release also includes an upgraded code editor: CodeMirror 6. Unlike its predecessor, this version is modular, allowing for the loading of only necessary components, including language support, which results in better performance. Furthermore, CodeMirror 6 is more extensible.
Other notable updates in JupyterLab 4.0 consist of:
- Real-time collaboration available as a separate package
- Simplified management and installation of extensions
- Support for custom package repositories
- A more powerful search and replace functionality, accessible via CTRL+SHIFT+H
- Enhanced customization options
- A cell editing toolbar
As you can see, JupyterLab 4.0 offers a range of productivity and performance enhancements. If you haven't updated yet, now is the perfect time.
Congratulations on reaching the end of this article! Engaging with coding enthusiasts like you is a delight. If you're keen on learning Python, consider checking out an incredible Python deck I've created. Join the 4,000 developers following Tom Smykowski! For just $5 a month, you can gain access to all Medium articles, allowing Tom to continue sharing insights about JupyterLab and Python. Become a member today!