---
title: Using uv with Jupyter
description:
  A complete guide to using uv with Jupyter notebooks for interactive computing, data analysis, and
  visualization, including kernel management and virtual environment integration.
---

# Using uv with Jupyter

The [Jupyter](https://jupyter.org/) notebook is a popular tool for interactive computing, data
analysis, and visualization. You can use Jupyter with uv in a few different ways, either to interact
with a project, or as a standalone tool.

## Using Jupyter within a project

If you're working within a [project](../../concepts/projects/index.md), you can start a Jupyter
server with access to the project's virtual environment via the following:

```console
$ uv run --with jupyter jupyter lab
```

By default, `jupyter lab` will start the server at
[http://localhost:8888/lab](http://localhost:8888/lab).

Within a notebook, you can import your project's modules as you would in any other file in the
project. For example, if your project depends on `requests`, `import requests` will import
`requests` from the project's virtual environment.

If you're looking for read-only access to the project's virtual environment, then there's nothing
more to it. However, if you need to install additional packages from within the notebook, there are
a few extra details to consider.

### Creating a kernel

If you need to install packages from within the notebook, we recommend creating a dedicated kernel
for your project. Kernels enable the Jupyter server to run in one environment, with individual
notebooks running in their own, separate environments.

In the context of uv, we can create a kernel for a project while installing Jupyter itself in an
isolated environment, as in `uv run --with jupyter jupyter lab`. Creating a kernel for the project
ensures that the notebook is hooked up to the correct environment, and that any packages installed
from within the notebook are installed into the project's virtual environment.

To create a kernel, you'll need to install `ipykernel` as a development dependency:

```console
$ uv add --dev ipykernel
```

Then, you can create the kernel for `project` with:

```console
$ uv run ipython kernel install --user --env VIRTUAL_ENV $(pwd)/.venv --name=project
```

From there, start the server with:

```console
$ uv run --with jupyter jupyter lab
```

When creating a notebook, select the `project` kernel from the dropdown. Then use `!uv add pydantic`
to add `pydantic` to the project's dependencies, or `!uv pip install pydantic` to install `pydantic`
into the project's virtual environment without persisting the change to the project `pyproject.toml`
or `uv.lock` files. Either command will make `import pydantic` work within the notebook.

### Installing packages without a kernel

If you don't want to create a kernel, you can still install packages from within the notebook.
However, there are a few caveats to consider.

Though `uv run --with jupyter` runs in an isolated environment, within the notebook itself,
`!uv add` and related commands will modify the _project's_ environment, even without a kernel.

For example, running `!uv add pydantic` from within a notebook will add `pydantic` to the project's
dependencies and virtual environment, such that `import pydantic` will work immediately, without
further configuration or a server restart.

However, since the Jupyter server is the "active" environment, `!uv pip install` will install
package's into _Jupyter's_ environment, not the project environment. Such dependencies will persist
for the lifetime of the Jupyter server, but may disappear on subsequent `jupyter` invocations.

If you're working with a notebook that relies on pip (e.g., via the `%pip` magic), you can include
pip in your project's virtual environment by running `uv venv --seed` prior to starting the Jupyter
server. For example, given:

```console
$ uv venv --seed
$ uv run --with jupyter jupyter lab
```

Subsequent `%pip install` invocations within the notebook will install packages into the project's
virtual environment. However, such modifications will _not_ be reflected in the project's
`pyproject.toml` or `uv.lock` files.

## Using Jupyter as a standalone tool

If you ever need ad hoc access to a notebook (i.e., to run a Python snippet interactively), you can
start a Jupyter server at any time with `uv tool run jupyter lab`. This will run a Jupyter server in
an isolated environment.

## Using Jupyter with a non-project environment

If you need to run Jupyter in a virtual environment that isn't associated with a
[project](../../concepts/projects/index.md) (e.g., has no `pyproject.toml` or `uv.lock`), you can do
so by adding Jupyter to the environment directly. For example:

=== "macOS and Linux"

    ```console
    $ uv venv --seed
    $ uv pip install pydantic
    $ uv pip install jupyterlab
    $ .venv/bin/jupyter lab
    ```

=== "Windows"

    ```pwsh-session
    PS> uv venv --seed
    PS> uv pip install pydantic
    PS> uv pip install jupyterlab
    PS> .venv\Scripts\jupyter lab
    ```

From here, `import pydantic` will work within the notebook, and you can install additional packages
via `!uv pip install`, or even `!pip install`.

## Using Jupyter from VS Code

You can also engage with Jupyter notebooks from within an editor like VS Code. To connect a
uv-managed project to a Jupyter notebook within VS Code, we recommend creating a kernel for the
project, as in the following:

```console
# Create a project.
$ uv init project

# Move into the project directory.
$ cd project

# Add ipykernel as a dev dependency.
$ uv add --dev ipykernel

# Open the project in VS Code.
$ code .
```

Once the project directory is open in VS Code, you can create a new Jupyter notebook by selecting
"Create: New Jupyter Notebook" from the command palette. When prompted to select a kernel, choose
"Python Environments" and select the virtual environment you created earlier (e.g.,
`.venv/bin/python` on macOS and Linux, or `.venv\Scripts\python` on Windows).

!!! note

    VS Code requires `ipykernel` to be present in the project environment. If you'd prefer to avoid
    adding `ipykernel` as a dev dependency, you can install it directly into the project environment
    with `uv pip install ipykernel`.

If you need to manipulate the project's environment from within the notebook, you may need to add
`uv` as an explicit development dependency:

```console
$ uv add --dev uv
```

From there, you can use `!uv add pydantic` to add `pydantic` to the project's dependencies, or
`!uv pip install pydantic` to install `pydantic` into the project's virtual environment without
updating the project's `pyproject.toml` or `uv.lock` files.
