Top JupyterLab Extensions for Machine Learning Research


JupyterLab is primarily intended to be an extensible environment. Any JupyterLab component can be enhanced or customized with JupyterLab Extensions. New themes, file viewers and editors, or display devices that allow rich output in notebooks are some of the things they can offer. Keyboard shortcuts, system settings, menu items or command panel can be added via extensions. Extensions can depend on other extensions and provide an API for use by other extensions. JupyterLab is nothing more than a set of extensions that have no more privileges or power than any other custom extension. The JupyterLab extension is just a plug-and-play extension that expands your options to achieve your goals. Technically, the JupyterLab extension is a JavaScript library that can enhance the JupyterLab interface with various interactive features.

Top JupyterLab Extension
corrector

Debugging is a critical step in removing any potential problems from our code. Now that debugging in different IDEs is simple, you can do it right in your Jupyter notebook. Since it comes pre-installed with JupyterLab 3.x, there is no need to download it separately. It is supported by two cores as of now.

Google Drive for JupyterLab

We use Google Drive to store our data in the cloud so that we can access it at any time. Adding a button or command makes adding Google Drive to Google Colab easier. Similar to how you helped us use Google Drive in JupyterLab, this plugin will enable us to access Google Drive files from within our laptops.

The Google Drive file browser has been added by this add-on to the left sidebar of JupyterLab. JupyterLab will be able to access the files in your GDrive when you sign in to your Google account.

JupyterLab Celltags

Users can quickly create, inspect, and change notebook cell meta tags using the JupyterLab cell tags plugin. The add-on allows selecting each cell that matches a certain tag, allowing any operation to be performed on those cells. You do not need to download the JupyterLab celltags extension separately as it is officially included with JupyterLab 3.x.

JupyterLab System Monitor

We often run our programs on Jupyter notebooks without knowing how much memory is being used. As a result, our laptop often freezes and stops working due to memory issues. We will benefit from knowing the current CPU and memory consumption statistics. The Jupyter notebook add-on called the JupyterLab System Screen shows system data, including CPU and memory usage.

Tabnin JupyterLab

Writing code is complicated without autocomplete options, especially when you first start. In addition to the time spent entering method names, the lack of autocomplete promotes shorter naming patterns, which is not ideal.

For a development environment to be efficient, autocomplete is critical. Using machine learning, TabNine can reliably predict what you might want to type next before you start by filling in the names of methods or variables you’ve already started typing. This can include method names from libraries whose names you have forgotten, which saves a lot of time searching online.

JupyterLab spreadsheet

Sometimes you have to work with spreadsheets in your role as a data scientist or data engineer. Jupyter’s inability to natively read Excel files causes many programs to switch between using Jupyter for encoding and Excel for rendering.

This challenge has been expertly solved with a jupyterlab spreadsheet. Thanks to Jupyter Lab’s integrated Xls/xlsx spreadsheet viewability, we can find everything we need in one place.

JupyterLab Matplotlib

If you are a data scientist, Matplotlib is a Python library that you must master. It is a straightforward but effective Python program for data visualization. However, the interactive component is no longer there when we use Jupyter Lab.

Matplotlib can become interactive again with the jupyter-matplotlib plugin. Your awesome 3D chart will become interactive by enabling it with the %matplotlib magic command widget.

JupyterLab Jet

It would be unwise not to use Git when writing any code, no matter how simple. Git makes it possible to track changes over time, giving you peace of mind that your code isn’t lost, rewritten, or changed incorrectly. Without Git, programming basically plays with Murphy’s Law.

Jupiter’s Git plugin provides seamless integration into the software. It’s faster, clearer, and will encourage you to push code changes more frequently to use Git from within Jupyter. This may prevent you from losing work and enable you to make more precise adjustments that you can refer to in case of errors.

JupyterLab variable inspector

With breakpoints and kernel steppers, the debugger extension helps in solving problems. The values ​​of various objects, such as graphical elements and code variables, are detected via the Variable Inspector. A resource you’ll be happy to have the first time you encounter a problem. This is given during coding.

JupyterLab Templates

You can switch from Jupyter Notebooks to JupyterLab with this add-on. This plugin converts Jupyter notebook templates to Jupyter Lab, so you can keep using them. You may want to use some of the old Jupyter Notebook templates even if you are just getting started with Jupyter. This extra time will enable you to do so.

JupyterLab TensorBoard

The front-end plug-in for TensorBoard on JupyterLab is called JupyterLab TensorBoard. As a backend for a tensorboard, it makes use of the jupyter tensorboard project. By providing a graphical user interface for tensorboard to start, manage and stop jupyter interface, it facilitates collaboration between jupyter notebook and tensorboard (a visualization tool for tensorflow).

Jupyter ML Workspace

A web-based and comprehensive integrated development environment built explicitly for machine learning, data science is known as an ML workspace.

It allows you to efficiently create ML solutions on your own devices and is easy to deploy. This workspace is a general purpose solution for programmers that comes preloaded with a variety of well-known data science libraries (such as Tensorflow, PyTorch, Keras, Sklearn) and development tools (such as Jupyter, VS Code, and Tensorboard), flawlessly configured, optimized, and integrated.

JupyterLab jupytext

Some Jupytext commands are added to the command palette with this extension. Although it is a modest feature, it can help with navigating a notebook. It can be used to choose the perfect text/ipynb match for your laptop.

JupyterLab nbgather

The JupyterLab add-on called nbgather provides tools for debugging, finding missing code, and comparing code versions. The add-on stores a history of all the code you’ve run along with any output you create in the notebook’s metadata. After downloading the extension, you can arrange and compare different code versions.

Since nbgather is still in the early stage of development, there may be some bugs. If you want to have organized and consistent notes, it is worth a try.

JupyterLab NBdime

You can compare and merge Jupyter notebooks using the functionality provided by this JupyterLab add-on. He can access and communicate notebooks intelligently because he is familiar with the structure of notebook papers.

Here is a quick summary of the main characteristics:

  • Easily compare laptops using the terminal
  • Combine three laptops with automatic conflict resolution
  • See a richly illustrated comparison of laptops.
  • Provide a 3-way integration tool for laptops on the web.
  • Display one laptop in a convenient terminal format.
JupyterLab Voyager

To see CSV and JSON data in Voyager 2, use the JupyterLab MIME viewer add-on called Voyager. It is an easy way to visualize data. The connection to Voyager that this plugin provides is minimal.

JupyterLab LaTeX

The bibliography is based on BibTeX, although it can also be customized. The JupyterLab add-on called LaTeX enables you to modify LaTeX scripts in real time. The Xelatex extension is used on the server, but you can adjust it by changing the jupyter config.py file.

Another customizable feature is the ability to execute arbitrary code using external shell commands.

JupyterLab HTML

This is a mime viewer for JupyterLab that displays HTML files in an IFrame tab. By double clicking on the on.html files in the file browser, you can inspect the rendered HTML. The JupyterLab tab opens to display the files.

JupyterLab Table of Contents

Although it may not seem like a particular technical feature, JupyterLab’s Table of Contents add-on can be very useful when scrolling and looking for information.

When you have a notebook or markdown document open, it automatically creates a table of contents in the left section. The address in question can be found by scrolling the document to the clickable entries.

JupyterLab foldable titles

Collapsible headings are a value addition provided by headers. The created caret icon can be clicked to the left of the header cells, or a shortcut can be used to collapse or uncollapse a specific header cell (for example, a tag cell that begins with several “#”).

Jupiter Dash

The Jupyter Dash Library makes it easy to create Dash applications from Jupyter environments (eg Classic Notebook, JupyterLab, Visual Studio Code notebooks, nteract, PyCharm notebooks, etc.).

Its many useful properties include:

  • Block-free execution
  • External, built-in and JupyterLab display options
  • Quick Reload is the ability to instantly update the currently executing web application when modifications are made to the program’s code.
  • The little user interface for reporting errors caused by ownership check failures and exceptions produced within callbacks is called error reporting.
  • Uncover proxy in Jupyter
  • Manufacturing Post
  • Dash workspaces for businesses
JupyterLab SQL

The latter provides a SQL user interface to JupyterLab using the jupyterlab-SQL extension. Using the point-and-click interface, you can explore your tables; With custom queries, you can read and edit your database.

References:

  • https://neptune.ai/blog/jupyterlab-extensions-for-machine-learning
  • https://www.tabnine.com/blog/top-jupyterlab-extensions/
  • https://towardsdatascience.com/10-jupyter-lab-extensions-to-boost-your-productivity-4b3800b7ca2a
  • https://github.com/mauhai/awesome-jupyterlab
  • https://www.analyticsvidhya.com/blog/2021/05/5-must-have-jupyterlab-extension-for-data-science/


Asif Razak is an AI journalist and co-founder of Marktechpost, LLC. He is a visionary, entrepreneur, and engineer who aspires to use the power of artificial intelligence for good.

Asif’s latest project is the development of an artificial intelligence media platform (Marktechpost) that will revolutionize how we find relevant news related to artificial intelligence, data science and machine learning.

Asif was featured by Onalytica in “Who’s Who in AI? (Influential Voices & Brands)” as one of the “Influential AI Journalists” (https://onalytica.com/wp-content/uploads/2021/09/Whos-Who- In-AI.pdf). His interview was also featured by Onalytica (https://onalytica.com/blog/posts/interview-with-asif-razzaq/).



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