Installing a Jupyter pre-release version in Cursor requires pip and a few terminal commands

A pre-release version of Jupyter is a development build released before the official stable version. It contains new features or bug fixes that haven't been formally released yet. To install one in Cursor (the AI-powered code editor), you use pip — Python's package manager — to pull the pre-release directly from PyPI (Python Package Index) instead of the standard stable release.

The process takes about five minutes and works on Windows, macOS, and Linux. You'll need Python and pip already installed on your machine, and you should be comfortable opening a terminal or command prompt.

Key Takeaways

  • Pre-release versions are installed using pip with the --pre flag, which tells pip to accept development builds.
  • You can install into your system Python or into a virtual environment; a virtual environment keeps pre-release packages isolated from your other projects.
  • The command pip install --pre jupyter installs the latest pre-release; you can specify an exact version if you need a particular build.
  • After installation, launch Jupyter from the terminal with jupyter notebook or jupyter lab depending on which interface you want.
  • If the pre-release breaks something, you can downgrade to the stable version by running pip install jupyter==X.Y.Z with the stable version number.

Check your Python and pip installation first

Before you install anything, confirm that Python and pip are available on your system. Open a terminal (on macOS or Linux) or Command Prompt (on Windows) and run:

python --version and pip --version

You should see version numbers for both. If either command is not recognized, you need to install Python first from python.org. The installer includes pip by default.

Note which Python version you have. Jupyter pre-releases usually support Python 3.8 and newer, but check the release notes on the Jupyter GitHub repository if you're running an older version.

Create a virtual environment (recommended)

A virtual environment is a separate folder where Python packages live, isolated from your system Python. This prevents a pre-release version from interfering with other projects that depend on the stable Jupyter version.

To create one, run:

python -m venv jupyter-prerelease

Then activate it. On macOS or Linux:

source jupyter-prerelease/bin/activate

On Windows:

jupyter-prerelease\Scripts\activate

Your terminal prompt should now show (jupyter-prerelease) at the start. Any packages you install from here on stay in this environment only.

Install the Jupyter pre-release with pip

With your virtual environment active (or without one, if you prefer to install system-wide), run:

pip install --pre jupyter

The --pre flag tells pip to accept pre-release versions. Without it, pip installs only stable releases. This command pulls the latest pre-release build from PyPI and installs it along with its dependencies.

If you want a specific pre-release version instead of the latest, you can specify it:

pip install --pre jupyter==4.0.0rc1

Check the Jupyter releases page on GitHub to find the exact version number of the pre-release you want. The installation takes a minute or two depending on your internet speed and machine.

Verify the installation and launch Jupyter

After pip finishes, confirm the pre-release installed correctly:

jupyter --version

You should see a version number that includes "rc" (release candidate) or "a" (alpha) or "b" (beta) — the markers that identify it as a pre-release.

To start Jupyter, run either:

jupyter notebook for the classic notebook interface, or

jupyter lab for JupyterLab, the newer interface.

Your browser should open automatically to http://localhost:8888 or a similar local address. If it doesn't, copy the URL from the terminal output and paste it into your browser.

Using Jupyter inside Cursor

Cursor can run Jupyter notebooks directly if you have the Jupyter extension installed. Open Cursor, create or open a file with a .ipynb extension (Jupyter's notebook format), and Cursor will detect it and offer to run cells.

If Cursor doesn't recognize your pre-release Jupyter installation, check that Cursor's Python interpreter setting points to the virtual environment where you installed the pre-release. Go to Cursor's settings, search for "Python: Default Interpreter Path", and set it to the Python executable inside your virtual environment folder.

Downgrade if the pre-release causes problems

Pre-release versions are tested but not as thoroughly as stable releases. If something breaks, downgrade to the latest stable version:

pip install jupyter==4.0.0 (replace 4.0.0 with the current stable version number)

Check the Jupyter releases page to find the latest stable version number. This command uninstalls the pre-release and installs the stable version in its place. Your notebooks and settings are not affected.

Frequently Asked Questions

Can I have both the pre-release and stable versions of Jupyter installed at the same time?

Not in the same Python environment. But you can create two separate virtual environments — one with the pre-release and one with the stable version — and switch between them by activating the one you need. This is the safest way to test a pre-release without risking your main projects.

What if pip says "no matching distribution found" when I try to install with --pre?

This usually means there is no pre-release version available at the moment, or the version you specified doesn't exist. Run pip index versions jupyter to see all available versions, including pre-releases. If none are listed, the Jupyter team may not have released a pre-release build yet.

Do I need to reinstall the pre-release after restarting my computer?

No. Once installed, Jupyter stays installed in your virtual environment or system Python. You only need to activate the virtual environment again (if you used one) before running Jupyter. The installation is permanent until you uninstall it.

How do I know if a pre-release is stable enough to use for real work?

Check the release notes on the Jupyter GitHub repository. Pre-releases marked "rc" (release candidate) are usually close to stable. Alpha and beta versions have more known issues. Read the changelog to see what changed and whether those changes affect your workflow. If you're unsure, test it in a separate virtual environment first.