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Rupam Golui
Rupam Golui Subscriber

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Why You Should Use uv Inside Jupyter Notebooks

#python #jupyter #datascience #productivity

If you’ve ever worked with Jupyter Notebooks, you know the pain:

  • One notebook runs on Python 3.10, another wants 3.11.
  • Some depend on torch==2.1.0, others break unless it’s 2.0.1.
  • And don’t even get me started on dependency conflicts when you’re switching between projects.

Traditional tools like pip + venv work fine, but they can feel slow and clunky. Enter uv, a blazing-fast Python package manager + environment manager.

Think of uv as the next-gen replacement for pip/venv/poetry. It makes project setup faster, cleaner, and way more consistent. And yes, you can use it seamlessly with Jupyter Notebooks.

Here’s how to set it up 👇

Setup Instructions

We’ll register a new Jupyter kernel that uses a uv-managed environment.

1. Install project dependencies

Inside your project folder:

uv install
# or
uv sync
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💡 Requires uv installed globally. If you don’t have it yet:

curl -LsSf https://astral.sh/uv/install.sh | sh
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or follow the official install guide.

2. Register a Jupyter kernel for your project

For example, let’s say we’re working on Virtus (my deepfake detection model):

uv run python -m ipykernel install --user --name vitrus --display-name "Python (vitrus)"
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This creates a new Jupyter kernel tied directly to your uv environment.

3. Add the new kernel to Jupyter

  • In PyCharm: just open the .ipynb file and select Python (myenv) from the top-right kernel selector.

Screenshot of PyCharm Jupyter Notebook kernel selector with

  • In Jupyter Lab / Notebook: switch kernels from the dropdown menu to use your shiny new environment.

Screenshot of JupyterLab interface showing the kernel selection dropdown, with

4. Remove old kernels

After your work is done, Clean up the messy leftovers:

jupyter kernelspec list
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Output will look like:

Available kernels:
  python3           /home/you/.local/share/jupyter/kernels/python3
  lopt              /home/you/.local/share/jupyter/kernels/lopt
  vitrus            /home/you/.local/share/jupyter/kernels/vitrus
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To remove one:

jupyter kernelspec uninstall vitrus
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Why bother with uv?

  • Speed: installs packages ridiculously fast (thanks to Rust).
  • Isolation: each project gets a clean, dedicated env.
  • Reproducibility: uv.lock means no “works on my machine” nonsense.
  • Jupyter-friendly: easy kernel registration, no hacky workarounds.

👉 My recommendation: set up your Jupyter projects with uv, and you’ll avoid 90% of Python environment headaches. Do it once per project and you’re golden.

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